\section{Introduction}
\label{sec:intro}
The fast spread of misinformation is an enormous challenge both for society and individuals, with great impact on democracy.
Severe and fatal consequences can be observed in relation to misinformation shared on social media related to COVID-19, with mistrust sowed in health measures required for combating a pandemic. With this in mind, \citet{pennycook_fighting_2020} even go so far as calling it a `matter of life and death'. 
Remembering the grave consequences, the need for digital misinformation interventions to slow down its propagation is evident. Those technical approaches can be divided roughly into two main steps \citep{potthast_stylometric_2018}: The (automatic) detection of misinformation and, second, the implementation of countermeasures as a concrete decision on what to do after successful detection.
A great deal of research exists on detecting misinformation, which is often based on machine learning algorithms (e.g., \citep{helmstetter_weakly_2018, brasoveanu_semantic_2019, shu_fake_2017}). Such algorithms are typically so called `black box' algorithms, which -- while producing promising results with regards to detection accuracy -- are not transparent in their reasoning. In order to make an algorithm's decisions transparent to users, interventions may profit from using `white box' algorithms/explainable AI, which give greater insights into how the algorithm behaves and what variables influence the model \citep{chen_spread_2022}. The reliance on these automatic detection measures is increasing. For example, because of COVID-related increase in traffic, Twitter has increased their use of machine learning and automation against misinformation \citep{gadde_update_2020}.
Complementary research has been done on the implementation of concrete interventions after successful automatic detection (e.g., \citep{bhuiyan_nudgecred_2021, roozenbeek_fake_2019,saltz_encounters_2021}). Those interventions are available in a wide range: Some aim at efficiently and automatically deleting content before exposure, others try to educate users by showing corrections, or flagging problematic contents. While there is a large and heterogeneous field of interventions, they have a direct impact on end users of social media, as they focus on whether and how to communicate their output and findings, for instance via information visualization. Although promising approaches have been established, the ongoing challenge of users being confronted and influenced by misinformation on  diverse social media platforms such as TikTok, Twitter, Instagram, Facebook and Co. suggests a need for further systematic design, implementation, and evaluation of effective digital interventions.

This review study aims to systematize knowledge on digital user-centered misinformation interventions.
For better readability we define misinformation as an umbrella term including misleading information that has been created deliberately, often defined as `disinformation' or `fake news', as well as unintentionally, frequently referred to as `misinformation'. This is in accordance with other researchers performing systematic reviews in that context while dealing with a wide set of terms \citep{chen_spread_2022,wang_systematic_2019,almaliki_online_2019,li_health_2022}. For example, \citet{li_health_2022} justify the use of misinformation as an umbrella term by stating that the majority of studies use this term to encompass different types of misleading information in general without denying the proper distinction between disinformation and misinformation.
While the term `misinformation intervention' has already been established by other researchers \citep{bak-coleman_combining_2022,saltz_encounters_2021,saltz_misinformation_2021}, we define \textit{user-centered misinformation interventions} as digital countermeasures that go beyond a purely algorithmic back-end solution and exert a direct influence on the user in the form of information presentation or information withholding. Accordingly, we do not include approaches that deal exclusively with the automatic detection of misinformation without describing the subsequent communication to the user. 
 
We provide a taxonomy that classifies and aggregates interventions regarding multiple relevant dimensions, such as time of intervention, addressed platform, and the thorough differentiation between intervention categories (e.g., correction, binary labeling, transparent indicators) to help identify promising research directions and encourage cross-disciplinary transferability. Researchers are faced with a very diverse research landscape on user-centered countermeasures which is spread across multiple disciplines, such as computer science, human-computer interaction, information systems, psychology, communication sciences, journalism, or even medical research. Hence, we address the challenge of gaining an overview and help build on existing research while considering and learning from current insights of different relevant disciplines as well as research on different social media platforms. Thereby, we seek to facilitate informed decision-making of researchers and practitioners when analyzing, designing, and evaluating (novel) digital countermeasures to combat misinformation.
We are especially interested in approaches using non-binary interventions, meaning interventions that communicate to users how an algorithm arrives at its results (i.e., white box algorithms) instead of giving a binary answer (i.e., misleading, not misleading). There is evidence that a more user-centered approach, transparently assisting users in their own assessment of misinformation, is more promising than a binary approach that provides social media posts with a label stating `This is/isn't misinformation' \citep{kirchner_countering_2020}. Hence, within this systematization of knowledge, we place an additional emphasis on transparent approaches.

While the topic of misinformation has been studied in systematic reviews, e.g. regarding specific contexts such as health \citep{li_health_2022} or political misinformation \citep{jerit_political_2020}, existing literature reviews on interventions against misinformation and similar phenomena focus on more general overviews. For example, a related literature map by \citet{almaliki_online_2019} focuses on the research field of misinformation and provides a general overview rather than analyzing the characteristics of concrete interventions and comparing the different approaches. They state that ``less than 2\% of the selected papers proposed digital intervention techniques'', while our focus lies on those studies that fall into the 2\% as a promising subgroup of interventions with growing research interest . Furthermore, when literature reviews deal with concrete interventions, they often focus on the detection step and machine learning interventions (e.g., \citep{caled_digital_2021, rana_review_2018,xu_unified_2021,guo_future_2021,zhou_survey_2021,zubiaga_detection_2019,mirsky_creation_2022}) instead of user-centered interventions. A first systematic overview on strategies against misinformation including countermeasures with a direct influence on end users was given by \citet{chen_spread_2022} who differentiate between five broad categories of solutions: message-based, source-based, network-based, policy-based, and education-based approaches. We complement their findings by (a) shedding light on user-centered aspects of concrete misinformation interventions by performing an in-depth analysis of their design, implementation, and evaluation. Thereby, we (b) specifically discuss and categorize characteristics impacting end users such as the intervention design, user interaction, and timing of the intervention. We further complement the existing research landscape by (c) performing a review on publications of diverse disciplines and not limited to a specific period until 2022, searching three major databases. To our knowledge, a systematization of knowledge on specific user-centered misinformation interventions has yet not been conducted.

Our overarching goal is to deeply examine and classify misinformation intervention studies in terms of methodological characteristics in study design and evaluation, content characteristics of user interventions, and derived trends and challenges for future research.
Addressing that goal, all our considerations lead us to the following research questions:

\begin{enumerate}
    \item[RQ1:] \textit{What are typical methodological characteristics of existing studies on misinformation interventions?}
    \item[RQ2:] \textit{How do existing forms of user-centered misinformation interventions assist users in dealing with misinformation online?}
    \item[RQ3:] \textit{Which trends and chances for future research can be derived from the existing literature?}
\end{enumerate}

The paper is structured as followed: First, we present our methodology of a systematic literature review and the procedure of deriving a taxonomy of user-centered misinformation interventions (see Section \ref{sec:meth}). Then, we present our results, including methodological aspects of analyzed publications such as addressed formats and platforms, applied methods of user studies, sample size and participant details (see Section \ref{sec:methodologicaloverviews}). Then we present our taxonomy (see Section \ref{sec:taxo}), distinguishing nine intervention designs, active versus passive user interaction and five points in time at which an intervention can be applied. We further discuss typical co-occurrencies of intervention characteristics (see Section \ref{sec:coocc}), and transparency as a specific measure to facilitate users in dealing autonomously with misinformation (see Section \ref{sec:transparen}). Lastly, we present `nudging' as a concept applied in many extracted publications (see Section \ref{sec:digitnudge}). 
In Section \ref{sec:disc}, we answer our research questions regarding the design of user-centered misinformation interventions, methodological characteristics of existing studies, and the derived trends, open questions and challenges for future research.

\section{Methodology}
\label{sec:meth}
In this section, we present our methodological approach of performing a systematic literature review, comprising of the identification and screening of relevant literature (Section \ref{sec:identificationlit}) and the thorough analysis and structuring of publications and interventions included therein (Section \ref{sec:develtaxo}).
\subsection{Identification of Literature}
\label{sec:identificationlit}
To identify and categorize relevant literature on misinformation interventions, we performed a systematic literature review, following the PRISMA guidelines \citep{page_prisma_2021} (see Figure \ref{fig:prisma_diagram}). \citet{schryen_knowledge_2020} state that literature reviews are important for ``developing domain knowledge'' and to identify knowledge-building activities, such as synthesizing, aggregating evidence, criticizing, theory building, identifying research gaps, and developing a research agenda. In accordance with these principles, we set up our literature search as follows: The initial search spans the ACM Digital Library, Web of Science, as well as the IEEE Xplore database. With this set of databases, we encompass a broad corpus of diverse literature as well as the ten conferences and journals listed by Google Scholar as the best regarding human computer interaction: ACM Conference on Human Factors in Computing Systems (CHI), IEEE Transactions on Affective Computing, Proceedings of the ACM on Interactive Mobile, Wearable and Ubiquitous Technologies (IMWUT),  Proceedings of the ACM on Human-Computer Interaction (PACM), International Journal of Human-Computer Studies (IJHCS), ACM/IEEE International Conference on Human Robot Interaction (HRI), ACM Conference on Computer-Supported Cooperative Work \& Social Computing (CSCW), IEEE Transactions on Human-Machine Systems, Behaviour \& Information Technology (BIT), and the ACM Symposium on User Interface Software and Technology (UIST). The search took place in November 2022 and only papers that were published until that date could be considered. All publication years up to this date have been included, however, there were no relevant publications for our final set concerning content-wise inclusion and exclusion criteria before 2011.

The search term consists of two parts: The first part are terms included in or related to our umbrella term of `misleading information'. The second part includes synonyms for `intervention' and related concepts addressing user-centered measures. Only papers containing at least one term of each part in their title or abstract were included in our search. We did not filter for a publication year. The complete search term is the following:

\textit{((rumour* OR rumor* OR "misleading
information" OR "fake news" OR "false news"
OR misinformation OR disinformation OR "news credibility")
AND (combat* OR correct* OR interven* OR countermeasur* OR
counteract* OR treatment OR relief OR educat* OR warning OR nudg* OR
user-centered OR "media literacy"))
}

\begin{figure}
    \centering
    \includegraphics[width=0.6\textwidth]{SoKMisinfo_PRISMA_Diagram.png}
    \caption{PRISMA Diagram demonstrating our data flow within the systematic literature review (created with the template from \citep{page_prisma_2021})}
    \label{fig:prisma_diagram}
\end{figure}

The term was modified to adhere to database requirements and to run comparable searches. Furthermore, because Web of Science returned many results, the term was adjusted to exclude obviously irrelevant disciplines (e.g., chemistry). The search returned 471 results from the ACM Digital Library, 440 results from IEEE Xplore and 2,946 results from Web of Science, in total 2857 results. After removing 136 duplicates, we screened 3,721 records, of which we excluded 2,994.

Records were removed for a multitude of reasons: First, some words of the search term had multiple meanings, which is why papers using a different interpretation were excluded. Furthermore, some technical terms have different meanings in different fields, for example, network science uses the term rumor in the context of nodes spreading information (e.g., \citep{buchegger_effect_2003}).
Second, when papers concentrated solely on the technical detection step with no involvement of the user at all, e.g., machine learning approaches focusing on increasing the detection rate, they were excluded.
Furthermore, interventions that took a network-based approach, for example by simulating which nodes to delete in order to reduce the spread of misinformation, were also excluded.
In addition, we excluded psychological experiments without concrete reference to misinformation as well as surveys and questionnaires exploring background information (e.g., Which demographics are susceptible to misinformation?).
Additionally, we decided to exclude reviews.

727 papers were sought for retrieval, of which 28 were removed because they could not be accessed, leaving us with a total of 699 publications that were assessed for eligibility. In the last step, a total of 581 papers were finally sorted out, 469 thereof because of the aforementioned criteria. Another 112 papers were excluded because they were too general, including papers that did not meet this review's focus because the intervention occurred long before the actual usage of social media, like educational school lectures, trainings, or serious games, but also implementations that focus exclusively on psychological phenomena (e.g., Do corrections of content generate reactance?). 
The final set of papers contained 108 items which were included in our analysis and were categorized according to our taxonomy.
\subsection{Development of a Taxonomy}
\label{sec:develtaxo}
For the development of the taxonomy, we first collected different relevant dimensions to compare and differentiate studies on user interventions within the context of misinformation. We developed those categories in an iterative process of brainstorming sessions with two researchers with expertise in computer science, psychology, and human-computer interaction based on already familiar studies within the field of interest (e.g., \citep{bhuiyan_nudgecred_2021, kirchner_countering_2020}). First, we defined our target group: researchers and practitioners interested in analyzing, designing, and evaluating digital countermeasures to combat misinformation may potentially benefit from our taxonomy. To assist the target groups, several characteristics are particularly relevant as they provide information on (1) the intervention design, (2) the form of user interaction, and (3) the timing of the intervention. Categories were complemented and adjusted iteratively while identifying and reading new papers: For instance, when reading multiple papers that differ regarding time of intervention, this category was included and all relevant papers were categorized accordingly. Additionally, minor modifications to the categories were made during the process of reading and categorizing the articles when deemed necessary. Further, we looked at how user-centered interventions were categorized in other contexts with sensible information (e.g., cybersecurity) in systematic reviews \citep{franz_sok_2021}. The resulting final table can be found in the appendix (see Table \ref{tab:sokfinaltable}, App.). 
The papers were systematically collected and categorized by two researchers with expertise in computer science, psychology and human-computer interaction. When disagreeing on a categorization, the study was discussed until agreement. A study can be sorted into several categories and the subcategories are generally not mutually exclusive (e.g., some interventions may combine the intervention categories `highlighting design' and `binary label' and others compare a `correction' with `showing indicators').

\section{Results: The Landscape of User-centered Misinformation Interventions}
\label{sec:res}
In this section, a detailed analysis of the literature review is presented. First, we give an overview regarding \textit{methodologies} used by studies on user interventions (see Section \ref{sec:methodologicaloverviews}). We then provide a \textit{taxonomy of interventions to assist users in dealing with misinformation}, by categorizing and clustering the identified research sample in distinct dimensions (see Section \ref{sec:taxo}). Further, \textit{co-occurrencies} (see Section \ref{sec:coocc}) of similar attributes of interventions are identified and discussed. Finally, we highlight how \textit{transparency} (see Section \ref{sec:transparen}) is used to assist users in dealing autonomously with misinformation, and present the concept of \textit{digital nudging} (see Section \ref{sec:digitnudge}) as a trending digital countermeasure.

\subsection{Methodological Characteristics}
\label{sec:methodologicaloverviews}
To provide an overview of research methods typically used in the field of user-centered interventions to assist in dealing with misinformation, details of the respective study designs were collected. 
All studies were published between 2011 and 2022.
First, we were interested in the diverse set of addressed concepts as the umbrella term of misleading information covers multiple relevant wordings. In total, 89 publications referred to either \textit{misinformation, disinformation or misleading information}. Further, 4 publications were specifically interested in \textit{rumours} and 10 publications addressed the concept of \textit{news credibility}. Other publications  for example, referred to myths, propaganda, or controversial topics.
Out of a total of 108 included papers, 11 present exclusively \textit{conceptual} ideas of interventions. In contrast, the remaining studies collected empirical data in the form of \textit{laboratory experiments} (12 publications), \textit{web-based experiments} (63 publications), \textit{field studies} (6 publications), \textit{surveys} (19 publications), and \textit{interviews} (9 publications). In some cases, there is a combination, e.g., of survey and interview or of laboratory experiment and web-based experiment within one publication.
Regarding sample size, the empirical studies range from small groups of participants (<20 e.g., \citep{brashier_timing_2021,challenger_covid-19_2022,gao_label_2018,komendantova_value-driven_2021}) to large-scaled representative groups with far over 1,000 participants (e.g., \citep{autry_correcting_2021,jia_understanding_2022,seo_trust_2019}). You can find a visualization of sample sizes in Figure \ref{fig:samplesize}. Looking more closely at the participants, a bias can be clearly discerned, as the majority of students report having surveyed U.S. adults and college students as participants. However, there are also isolated studies that either address a very specific (vulnerable) target group (e.g., teenagers \citep{axelsson_learning_2021}) or make a comparison between several countries (e.g., \citep{almaliki_misinformation-aware_2019}).

\begin{figure}
    \includegraphics[width=\textwidth]{samplesizeOhneID2.png}
    \caption{Sample sizes (log) of the individual studies broken down by study type including the median (Mdn). Note that a publication often contains multiple user studies.}
    \label{fig:samplesize}
\end{figure}

While 38 publications generate their interventions or concepts generically for all online content and platforms (category \textit{General}), others are developed and evaluated for specific platforms (see Figure \ref{fig:yearanzahl} for temporal distribution regarding platforms). Nevertheless, transferability to other platforms is often not excluded. 26 publications address interventions for \textit{Facebook}, 21 publications for \textit{Twitter}, and 3 publications for \textit{Instagram}. Another 11 publications deal with platforms that do not fall into one of the categories already mentioned (e.g., websites, arguments over an audio speaker, text documents, messenger forwards) and therefore were categorized as \textit{Other}. This corresponds to known research biases that show a focus on much-researched platforms such as Twitter. This is often justified by the already developed data situation and easier linkage to existing literature. Especially the great relevance of misinformation on newer social media platforms like TikTok in crises like the Russian-Ukrainian war shows that there is still a great need for research. When looking more closely at the addressed content format, we can see that most publications focus on \textit{social media posts} (59 publications), 29 on \textit{articles or text in general} and only a few on \textit{images} and \textit{videos} (5 publications each) while we observe a growing relevance of misinformation of exactly these formats. Additionally, there are a few exceptions that address a very specific format such as \textit{audio} (e.g., \citep{danry_wearable_2020}).

\begin{figure}
    \includegraphics[width=\textwidth]{year_anzahl.png}
    \caption{Number of papers published according to their addressed platform.}
    \label{fig:yearanzahl}
\end{figure}

A minority of the publications describe interventions that go beyond a low-fidelity prototype (e.g., in the form of screenshots) to include an actual implementation. Thus, \textit{no implementations} can be read from 69 publications, while 9 publications deal with \textit{browser plugins or browser extensions} (e.g., \citep{bhuiyan_nudgecred_2021,khivasara_fake_2020}). Eleven publications describe the implementation of a \textit{custom platform} (e.g., \citep{amin_visual_2021}), and 3 publications show a \textit{game-based implementation}.
Elaborate tools as are an important part for mitigating the spread of misinformation they can be part of a holistic solution. 
An example is `Verifi!' \citep{karduni_vulnerable_2019}, which provides an interface for dealing with misinformation on Twitter. The system consists of five display options, allowing for easy comparison between how real and questionable news sources report on a subject, for example by comparing the words or images used. Another example would be `Prta' \citep{martino_prta_2020} which provides the user with a tool that takes a text or URL as input and highlights propaganda techniques.

\subsection{A Taxonomy of User-centered Misinformation Interventions}
\label{sec:taxo} 
The wide range of addressed concepts, platforms and research areas shows that, on the one hand, a large number of conceptual ideas and empirical findings already exist for digitally supporting users in dealing with misinformation; on the other hand, these often differ fundamentally. In order to distinguish existing approaches from each other and to cluster commonalities, we have derived a taxonomy based on the identified literature. Therefore, we performed an in-depth analysis of interventions. For the interpretation of the following results it is important to notice that publications can contain multiple interventions -- in total, there were 154 interventions within the 108 publications. Those interventions were analyzed individually regarding the taxonomy characteristics, while previously reported methodological findings are valid for the entire publication and therefore did not distinguish between individual interventions.
In the following, the literature based categories of the taxonomy are explained in detail (see also Table \ref{tab:taxonomy} and Figure \ref{fig:taxonomy}):

\begin{table*}[]
\resizebox{\textwidth}{!}{%
\begin{tabular}{|l|lll|}
\hline
\rowcolor[HTML]{000000} 
{\color[HTML]{FFFFFF} \textbf{Category}}          & \multicolumn{1}{l|}{\cellcolor[HTML]{000000}{\color[HTML]{FFFFFF} \textbf{Definition}}}                                                                                          & \multicolumn{1}{l|}{\cellcolor[HTML]{000000}{\color[HTML]{FFFFFF} \textbf{Intervention examples}}}            & {\color[HTML]{FFFFFF} \textbf{Publications}} \\ \hline
\rowcolor[HTML]{656565} 
{\color[HTML]{FFFFFF} \textbf{Intervention design}} & \multicolumn{3}{l|}{\cellcolor[HTML]{656565}{\color[HTML]{FFFFFF} \parbox{22.5cm}{\vspace{.2\baselineskip}The intervention design distinguishes different countermeasures after the successful detection of misinformation. This includes general actions such as deleting content as well as concrete design decisions to encourage a learning effect.\vspace{.2\baselineskip}}}}                                                                                                        \\ \hline
Warning                                           & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Interventions that give an explicit warning that the content is (potentially) misleading\vspace{.2\baselineskip}}} & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Warning label; stop sign; ``This post was disputed''\vspace{.2\baselineskip}}} &     {\parbox{6cm}{\citep{andi_nudging_2020,ardevol-abreu_intentional_2020,bhuiyan_nudgecred_2021,bhuiyan_feedreflect_2018,clayton_real_2020,folkvord_effect_2022,freeze_fake_2021,gao_label_2018,grady_nevertheless_2021,hameleers_separating_2020,jennings_asymmetric_2021,kirchner_countering_2020,kreps_covid-19_2022,mena_cleaning_2020,moravec_appealing_2020,nekmat_nudge_2020,ozturk_combating_2015,park_experimental_2021,pennycook_implied_2020,von_der_weth_nudging_2020,sherman_designing_2021,seo_trust_2019,saltz_encounters_2021,porter_political_2022}}}                                         \\ \hline
Correction/debunking                              & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Interventions that correct/debunk misinformation\vspace{.2\baselineskip}}}                                                                                                                                                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Naturally occurring or artificially generated user comments or comments from officials that correct misinformation; Links to fact-checking websites; expert sources\vspace{.2\baselineskip}}}                                                                                & {\parbox{6cm}{\vspace{.2\baselineskip}\citep{aird_does_2018,autry_correcting_2021,bode_related_2015,bode_see_2018,challenger_covid-19_2022,dai_effects_2021,ecker_correcting_2011,ecker_reminders_2017,ecker_can_2020,ecker_you_2020,gesser-edelsburg_correcting_2018,hameleers_picture_2020,hameleers_separating_2020,huang_when_2022,jahng_they_2021,kessler_debunking_2022,kim_eye_2021,kreps_covid-19_2022,kirchner_countering_2020,lee_effect_2022,liaw_maater_2013,lo_all_2021,martel_youre_2021,moon_how_2022,ozturk_combating_2015,pasquetto_social_2022,pluviano_misinformation_2017,porter_political_2022,rich_correcting_2020,sakhnini_review_2022,sangalang_potential_2019,schaewitz_combating_2020,smith_correcting_2019,sotirakou_toward_2022,sullivan_leveraging_2019,tanaka_exposure_2019,tanaka_toward_2013,thornhill_digital_2019,tseng_investigating_2022,tully_mobilizing_2020,van_der_meer_seeking_2020,vraga_i_2018,vraga_testing_2019,vraga_testing_2020,vraga_assessing_2021,vraga_creating_2022,vraga_effects_2021,vraga_using_2017,wahlheim_reminders_2020,wang_countering_2021,zhao_misinformation_2019}\vspace{.2\baselineskip}}}                                              \\ \hline
Showing indicators                                & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Interventions that display indicators for misinformation to achieve transparency\vspace{.2\baselineskip}}}                                                                                                                                                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}showing how old a video actually is; color relevant words for misinformation classification; generic tips to detect misinformation; infographic\vspace{.2\baselineskip}}}                                                                                &                {\parbox{6cm}{\vspace{.2\baselineskip}\citep{ayoub_combat_2021,bhuiyan_designing_2021,chiang_impact_2022,domgaard_combating_2021,furuta_fact-checking_2021,grandhi_by_2021,guess_digital_2020,hameleers_separating_2020,karduni_vulnerable_2019,martino_prta_2020,sakhnini_review_2022,schmid_digital_2022,sherman_designing_2021,von_der_weth_nudging_2020}\vspace{.2\baselineskip}}}                              \\ \hline
Binary labels                                     & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Interventions that label content as misinformation or true information without giving an explanation\vspace{.2\baselineskip}}}                                                                                                                                                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Tagging post as true or false; thumbs up/thumbs down; ``Prediction: It is Fake News!''\vspace{.2\baselineskip}}} &                  {\parbox{6cm}{\vspace{.2\baselineskip}\citep{aslett_news_2022,barua_f-nad_2019,brashier_timing_2021,ecker_can_2020,grady_nevertheless_2021,hawa_combating_2021,jahanbakhsh_leveraging_2022,jia_understanding_2022,khivasara_fake_2020,kreps_covid-19_2022,lee_effect_2022,lu_effects_2022,moravec_appealing_2020,pasquetto_social_2022,pennycook_implied_2020,porter_political_2022,sakhnini_review_2022,saltz_encounters_2021,schmid_digital_2022,sherman_designing_2021,sotirakou_toward_2022,thornhill_digital_2019}\vspace{.2\baselineskip}}}                            \\ \hline
Highlighting design                               & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Interventions that visually highlight relevant parts of a post for misinformation classification\vspace{.2\baselineskip}}} & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}highlight relevant words by color or size; color code tweets according to accuracy; highlight propaganda techniques using colors \vspace{.2\baselineskip}}}                                                                                &                    {\citep{amin_visual_2021,aslett_news_2022,bhuiyan_designing_2021,bhuiyan_feedreflect_2018,bhuiyan_nudgecred_2021,furuta_fact-checking_2021,jahanbakhsh_leveraging_2022,liaw_maater_2013,martino_prta_2020,sherman_designing_2021,thornhill_digital_2019}}                          \\ \hline
{\parbox{3cm}{\vspace{.2\baselineskip}Visibility reduction\vspace{.2\baselineskip}}}                              & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Interventions that reduce the visibility of misinformation\vspace{.2\baselineskip}}}                                                                                                                                                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Reducing opacity or size\vspace{.2\baselineskip}}}                                                                                &                            {\parbox{6cm}{\vspace{.2\baselineskip}\citep{ardevol-abreu_intentional_2020,bhuiyan_nudgecred_2021,bhuiyan_feedreflect_2018,kirchner_countering_2020,kreps_covid-19_2022,porter_political_2022,saltz_encounters_2021,sherman_designing_2021}\vspace{.2\baselineskip}}}                  \\ \hline
Removal                                           & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Interventions that hide or remove misinformation\vspace{.2\baselineskip}}}                                                                                                                                                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}deleting misinformation\vspace{.2\baselineskip}}}                                                                                &                  {\parbox{6cm}{\vspace{.2\baselineskip}\citep{saltz_encounters_2021}\vspace{.2\baselineskip}}}                            \\ \hline
Complicate sharing                                & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Interventions that include additional user effort before allowing to share misinformation\vspace{.2\baselineskip}}}                                                                                                                                                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}additional confirmation before sharing; require users to assess accuracy before sharing\vspace{.2\baselineskip}}} &                                {\parbox{6cm}{\citep{amin_visual_2021,jahanbakhsh_exploring_2021,kirchner_countering_2020,von_der_weth_nudging_2020}\\}}              \\ \hline
Specific visualization                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Interventions that use creative visualizations of relevant information\vspace{.2\baselineskip}}}                                                                                                                                                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}visualizing sentiment and controversy score of news articles; visualizing fact-checker decisions; platform based on social network analysis visualization; aggregate authentication measures; visualization of number of unvaccinated children with measles as fear correction; visualizing fact-checker decisions; infographic\vspace{.2\baselineskip}}}                                                                                &                                    {\parbox{6cm}{\vspace{.2\baselineskip}\citep{domgaard_combating_2021,jahanbakhsh_leveraging_2022,karduni_vulnerable_2019,kessler_debunking_2022,kim_controversy_2019,park_experimental_2021,pluviano_misinformation_2017,pourghomi_how_2017,safieddine_corporate_2016,schmid_digital_2022,sherman_designing_2021}\vspace{.2\baselineskip}}}          \\ \hline
\rowcolor[HTML]{656565} 
{\color[HTML]{FFFFFF} \textbf{User interaction}}  & \multicolumn{3}{l|}{\cellcolor[HTML]{656565}{\color[HTML]{FFFFFF} \parbox{15cm}{\vspace{.2\baselineskip}Interventions require varying degrees of interaction with the countermeasure\vspace{.2\baselineskip}}}}                                                                                                        \\ \hline
Active                                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Active interventions require users to actively interact with a countermeasure\vspace{.2\baselineskip}}} & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}click to confirm before sharing; overlay on Facebook/Twitter post; pop-up\vspace{.2\baselineskip}}} &                                  {\parbox{6cm}{\vspace{.2\baselineskip}\citep{agley_intervening_2021,amin_visual_2021,andi_nudging_2020,ardevol-abreu_intentional_2020,chen_examining_2022,folkvord_effect_2022,grady_nevertheless_2021,jahanbakhsh_exploring_2021,jahanbakhsh_leveraging_2022,khivasara_fake_2020,kirchner_countering_2020,kreps_covid-19_2022,pennycook_fighting_2020,porter_political_2022,saltz_encounters_2021,sharevski_two_2021,tsipursky_fighting_2018,von_der_weth_nudging_2020,vraga_testing_2020}\vspace{.2\baselineskip}}}            \\ \hline
Passive                                           & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Passive interventions can be potentially ignored while using social media\vspace{.2\baselineskip}}}                                                                                                                                                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}A label below a social media post; tooltip; correction in comment section; warning next to a post\vspace{.2\baselineskip}}}                                                                                &       {\parbox{6cm}{\vspace{.2\baselineskip}\citep{aird_does_2018,aslett_news_2022,autry_correcting_2021,ayoub_combat_2021,bhuiyan_designing_2021,bhuiyan_feedreflect_2018,bhuiyan_nudgecred_2021,bode_related_2015,bode_see_2018,brashier_timing_2021,clayton_real_2020,dai_effects_2021,domgaard_combating_2021,duncan_whats_2020,ecker_reminders_2017,ecker_correcting_2011,ecker_can_2020,ecker_you_2020,freeze_fake_2021,furuta_fact-checking_2021,gao_label_2018,gesser-edelsburg_correcting_2018,grady_nevertheless_2021,guess_digital_2020,hameleers_picture_2020,hameleers_separating_2020,huang_when_2022,jahng_they_2021,jennings_asymmetric_2021,jia_understanding_2022,kessler_debunking_2022,kim_combating_2019,kim_eye_2021,kirchner_countering_2020,lee_effect_2022,liaw_maater_2013,lu_effects_2022,martel_youre_2021,mena_cleaning_2020,moon_how_2022,moravec_appealing_2020,nekmat_nudge_2020,ozturk_combating_2015,park_experimental_2021,pasquetto_social_2022,pennycook_implied_2020,pennycook_shifting_2021,pluviano_misinformation_2017,pourghomi_how_2017,rich_correcting_2020,safieddine_corporate_2016,saltz_encounters_2021,sangalang_potential_2019,schaewitz_combating_2020,seo_trust_2019,sharevski_two_2021,sherman_designing_2021,smith_correcting_2019,sullivan_leveraging_2019,tanaka_exposure_2019,tanaka_toward_2013,thornhill_digital_2019,tseng_investigating_2022,tully_designing_2020,tully_mobilizing_2020,van_der_meer_seeking_2020,vraga_using_2017,vraga_testing_2019,vraga_i_2018,vraga_testing_2019,vraga_testing_2020,vraga_creating_2022,vraga_effects_2021,wahlheim_reminders_2020,wang_countering_2021}\vspace{.2\baselineskip}}}                                       \\ \hline
\rowcolor[HTML]{656565} 
{\color[HTML]{FFFFFF} \textbf{Timing}}            & \multicolumn{3}{l|}{\cellcolor[HTML]{656565}{\color[HTML]{FFFFFF} \parbox{15cm}{\vspace{.2\baselineskip}Digital misinformation interventions can address varying points in time within the social media usage\vspace{.2\baselineskip}}}}                                                                                                        \\ \hline
Pre exposure                                      & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Interventions that take place immediately before the exposure to (mis)information\vspace{.2\baselineskip}}}                                                                                                                                                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}accuracy nudge before a news-sharing task; Pro-Truth pledge to engage in more pro-social behavior; narrative fear appeal message to encourage health experts to correct health misinformation online; general warning message about misleading articles; generic infographic; protective message: ``Warning! Note: fake news can occur on Facebook. [...]''\vspace{.2\baselineskip}}} &                            {\parbox{6cm}{\vspace{.2\baselineskip}\citep{agley_intervening_2021,brashier_timing_2021,chen_examining_2022,clayton_real_2020,dai_effects_2021,domgaard_combating_2021,folkvord_effect_2022,grady_nevertheless_2021,guess_digital_2020,hameleers_separating_2020,karduni_vulnerable_2019,kirchner_countering_2020,moravec_appealing_2020,pennycook_fighting_2020,pennycook_shifting_2021,tanaka_toward_2013,tsipursky_fighting_2018,tully_designing_2020,vraga_assessing_2021,vraga_testing_2020}\vspace{.2\baselineskip}}}                  \\ \hline
During exposure                                   & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Interventions that take place during the usage of social media (and during the exposure to misinformation)\vspace{.2\baselineskip}}} & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}algorithmic corrections next to a post; user comments underneath a post; warnings; adding ``Rated False'' tag to article headline; credibility labels; highlighting indicators for propaganda; wearable reasoner giving AI-based feedback on claims\vspace{.2\baselineskip}}} &               {\parbox{6cm}{\vspace{.2\baselineskip}\citep{aird_does_2018,amin_visual_2021,andi_nudging_2020,ardevol-abreu_intentional_2020,aslett_news_2022,ayoub_combat_2021,bhuiyan_designing_2021,bhuiyan_feedreflect_2018,bhuiyan_nudgecred_2021,bode_related_2015,bode_see_2018,brashier_timing_2021,clayton_real_2020,danry_wearable_2020,duncan_whats_2020,ecker_can_2020,gao_label_2018,grady_nevertheless_2021,hameleers_picture_2020,hameleers_separating_2020,horne_trustworthy_2019,jahanbakhsh_leveraging_2022,jahng_they_2021,jennings_asymmetric_2021,jia_understanding_2022,kim_combating_2019,kim_eye_2021,kirchner_countering_2020,kreps_covid-19_2022,liaw_maater_2013,lo_all_2021,lu_effects_2022,mena_cleaning_2020,moon_how_2022,moravec_appealing_2020,nekmat_nudge_2020,ozturk_combating_2015,park_experimental_2021,pennycook_implied_2020,pluviano_misinformation_2017,porter_political_2022,seo_trust_2019,sharevski_two_2021,sherman_designing_2021,smith_correcting_2019,sullivan_leveraging_2019,thornhill_digital_2019,tully_mobilizing_2020,vraga_assessing_2021,vraga_i_2018,vraga_using_2017,vraga_creating_2022,vraga_effects_2021}\vspace{.2\baselineskip}}}                               \\ \hline
Post exposure                                     & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Interventions occurring after seeing misinformation\vspace{.2\baselineskip}}}                                                                                                                                                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}warnings after exposure to misinformation; responses by official health authority; debunking text based on debunking handbook including plausible scientific explanations to close gaps in mental models\vspace{.2\baselineskip}}}                                                                                &            {\parbox{6cm}{\vspace{.2\baselineskip}\citep{autry_correcting_2021,brashier_timing_2021,dai_effects_2021,ecker_you_2020,ecker_reminders_2017,ecker_correcting_2011,freeze_fake_2021,gesser-edelsburg_correcting_2018,grady_nevertheless_2021,huang_when_2022,kessler_debunking_2022,lee_effect_2022,lu_effects_2022,martel_youre_2021,pasquetto_social_2022,pennycook_shifting_2021,rich_correcting_2020,sangalang_potential_2019,schaewitz_combating_2020,tanaka_exposure_2019,tanaka_toward_2013,tseng_investigating_2022,van_der_meer_seeking_2020,vraga_testing_2019,vraga_testing_2020,wahlheim_reminders_2020,wang_countering_2021}\vspace{.2\baselineskip}}}                                  \\ \hline
At the moment of sharing                          & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Interventions taking place directly at the moment of sharing misinformation\vspace{.2\baselineskip}}}                                                                                                                                                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Encouraging to reflect on content before sharing; endorsing accuracy prompt: ``I think this news is accruate'' placed into sharing button; behavioral nudges using checkboxes to indicate whether a heading is accurate and to tag reasons via checklist at posting time; report of linguistic analysis as immediate feedback when sharing and possibility to cancel a tweet within 30 seconds\vspace{.2\baselineskip}}}                                                                                &         {\parbox{6cm}{\vspace{.2\baselineskip}\citep{amin_visual_2021,capraro_i_2022,jahanbakhsh_exploring_2021,jahanbakhsh_leveraging_2022,kirchner_countering_2020,von_der_weth_nudging_2020}\vspace{.2\baselineskip}}}                                     \\ \hline
On request of the user                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}Interventions that take place detached from social media platforms and have to be reached out to actively\vspace{.2\baselineskip}}}                                                                                                                                                            & \multicolumn{1}{l|}{\parbox{8cm}{\vspace{.2\baselineskip}web-app based on social network analysis for user exploration; Android application where user can enter URL or text for credibility assessment; system to analyze articles or URLs via interface or API\vspace{.2\baselineskip}}}                                                                                &          {\parbox{6cm}{\vspace{.2\baselineskip}\citep{barua_f-nad_2019,chiang_impact_2022,hawa_combating_2021,karduni_vulnerable_2019,khivasara_fake_2020,kim_controversy_2019,martino_prta_2020,pourghomi_how_2017,safieddine_corporate_2016,sakhnini_review_2022,schmid_digital_2022,sotirakou_toward_2022}\vspace{.2\baselineskip}}}                                    \\ \hline
\end{tabular}%
}
\caption{A taxonomy of user-centered misinformation interventions.}
\label{tab:taxonomy}
\end{table*}


\begin{figure}[h!]
    \centering
    \label{fig:taxo}
    
\begin{forest}
for tree={
    grow=east,
    growth parent anchor=east,
    parent anchor=east,
    child anchor=west,
    edge path={\noexpand\path[\forestoption{edge},->, >={latex}] 
         (!u.parent anchor) -- +(5pt,0pt) |- (.child anchor)
         \forestoption{edge label};}
         }
[Interventions, root
[\textbf{Time of intervention}, onode
    [On request of the user, tnode]
    [At the moment of sharing, tnode]
    [Post exposure, tnode]
    [During exposure, tnode]
    [Pre exposure, tnode]]
[\textbf{User interaction}, onode
     [Passive, tnode]
     [Active, tnode]]
[\textbf{Intervention design}, onode
     [Specific visualization, tnode]
     [Complicate sharing, tnode]
     [Removal, tnode]
     [Visibility reduction, tnode]
     [Highlighting design, tnode]
     [Binary labels, tnode]
     [Showing indicators, tnode]
     [Correction/debunking, tnode]
     [Warning, tnode]]
]
\end{forest}
\caption{A taxonomy for user-centered misinformation interventions.}
\end{figure}
\label{fig:taxonomy}

\subsubsection{Intervention Design} 
\label{sec:interventiontype}
The identified interventions on user-centered misinformation interventions vary greatly in their starting point. The digital support approaches and concepts are as diverse as the possibilities for protecting users from the effects of misinformation (e.g., deleting problematic content, warning, or strengthening media literacy). In an iterative process, nine intervention designs were identified based on the literature. Interventions could be assigned to multiple intervention designs, as they often used combinations.
The majority of interventions proposes or evaluates \textit{correction/debunking} of misleading contents (45 stand-alone interventions and 16 interventions in combination with other interventions designs within 51 publications) and often represents a quite natural behaviour of social media usage rather than an artificially generated technical countermeasure. For instance, many publications in that context evaluate whether corrections by users in the comment section of a post are effective in reducing belief in misleading content (e.g., \citep{martel_youre_2021}). Some of those interventions include a link to fact checking websites, where the misleading content is debunked. This can be implemented both naturally by users in the comment section posting debunking links or digital interventions automatically exposing users to debunking (e.g., link to correcting source or user rebuttal within a comment/reply to a social media post). 
Many interventions do not necessarily expose users to content debunking or rebuttals but give an explicit \textit{warning} that a content is (potentially) misleading (15 stand-alone interventions and 17 interventions in combination within 24 publications). Those warnings reach from warning labels like stop signs, to textual warnings, e.g., ``This post was disputed!''.

Misinformation interventions can have different objectives. One of these objectives is to strengthen media literacy. In these types of intervention, concrete assistance in the form of indicators, for example, is typical. By \textit{showing indicators} that support users in evaluating content, the aim is to achieve a learning effect. Fifteen interventions correspond to this intervention design (including 6 as stand-alone intervention; 14 publications), for example by showing how old a video actually is \citep{sherman_designing_2021} or by highlighting words in the text that were particularly relevant for automatic detection as misleading \citep{ayoub_combat_2021}. Other intervention designs of this type compile more generic tips that users can apply to detect misleading content \citep{domgaard_combating_2021,guess_digital_2020,hameleers_separating_2020}. This intervention design is especially relevant, as studies have shown that users prefer transparent approaches where there is a potential learning effect \citep{kirchner_countering_2020}.
However, in contrast to transparently showing indicators for misleading content, 26 interventions take a contrarian approach by assigning \textit{binary labels} to contents (including 10 stand-alone interventions; 22 publications). This can be implemented, for example, by tagging a content with a true or false tag, or thumbs up, thumbs down \citep{barua_f-nad_2019}. Other interventions give a probability in percent that a content is misleading \citep{khivasara_fake_2020}, and thus extend the framework of binary labels with richer information. Still, these labels have in common, that they do not provide a transparent explanation.

Again with a more transparent idea, some approaches use \textit{highlighting design} as an intervention design aims at facilitating potential learning effects. Eleven interventions (11 publications), for example, visually highlight relevant words for automatic classification within a social media post by color or size. For instance, \citet{bhuiyan_nudgecred_2021} color code tweets on Twitter according to their computed accuracy. In contrast, \citet{martino_prta_2020} highlight propaganda techniques (e.g., exaggeration, loaded language, or oversimplification) detected within a text, using different colors.
Indeed, despite highlighting components of content with colors, more \textit{specific visualizations} (11 interventions; 11 publications) can be considered a distinct intervention design. Visualization is a very effective way to provide information as it provides \textit{``the highest bandwidth channel from computer to the human''} \citep[][p.~2]{ware_information_2012} and is used for interventions in different contexts \citep{hartwig_nudging_2022}. Within the literature sample there is a diverse set of creative visualizations of information. For example, \citet{kim_controversy_2019} visualize the sentiment and controversy score of news articles within a conceptual study. In contrast, \citet{park_experimental_2021} visualize fact-checker decisions regarding textual rumours. Moreover, \citet{schmid_digital_2022} developed and evaluated a platform based on social network analysis of contents on Twitter for users to proactively assess misinformation through visualization.

While the previous intervention designs tend to give a feedback on top of the problematic content, one study evaluates the effect of \textit{removal of misinformation} by hiding or removing a questionable post altogether \citep{saltz_encounters_2021}.
Similarly but less rigorous is the attempt of \textit{visibility reduction} (12 interventions in 8 publications), e.g., by reducing opacity or size. 
Many intervention designs aim at preventing negative effects on people when confronted with misinformation or educating them to detect those contents themselves, as presented in the previous intervention designs. However, this can be extended to specifically preventing the spread of problematic content altogether, for instance via \textit{complicating sharing} (4 interventions in 4 publications). This may be implemented, for example, by including an additional confirmation before sharing or by nudging users to assess the accuracy of a content as they share it \citep{jahanbakhsh_exploring_2021}. These user-centered approaches stand in contrast to network-based approaches to prevent the spread of misinformation through computational techniques as described by \citet{chen_spread_2022}.

While most interventions could be assigned to one or more intervention designs presented, 41 interventions additionally used an intervention design that did not clearly fit the scheme (including 28 stand-alone interventions; 31 publications) while not appearing often enough as a distinct intervention design to represent an own type within the taxonomy. There are particularly unusual approaches, such as the development of wearable glasses that provide audio feedback on the truth of content \citep{danry_wearable_2020} or a study that evaluates the effect of priming participants by letting them rate the accuracy of a headline before exposure to more potentially misleading content as a nudge to think more sufficiently \citep{pennycook_fighting_2020}.
Among the \textit{other} category, there are eight interventions giving diverse kinds of information about misinformation and its detection immediately before exposure, e.g., in form of an infographic \citep{agley_intervening_2021}, a video tutorial \citep{axelsson_learning_2021}, a text about negative consequences of misinformation \citep{clayton_real_2020}, a debiasing message \citep{dai_effects_2021}, an awareness training \citep{moravec_appealing_2020}, or a Pro-Truth Pledge \citep{tsipursky_fighting_2018}. Three interventions display a star rating or score, for instance regarding credibility or sentiment \citep{duncan_whats_2020,kim_combating_2019,kim_controversy_2019}. Two interventions explicitly state to have integrated gamification elements \citep{almaliki_misinformation-aware_2019, sotirakou_toward_2022}.
As stated before, publications could be assigned to multiple intervention designs as they often used combinations or compared interventions of different types against each other (see Section \ref{sec:coocc}).
You can find a visualization of intervention designs in Figure \ref{fig:interventiontypeInsgesamt}.

\begin{figure}
    \centering
    \includegraphics[width=0.75\textwidth]{interventiondesigns_AnzahlInterventions.png}
    \caption{Number of interventions addressing common intervention designs while differentiating between approaches that use a single intervention design versus multiple types in combination. Instances <3 were excluded. A more detailed breakdown of the category `other' can be found in Section \ref{sec:interventiontype}.}
    \label{fig:interventiontypeInsgesamt}
\end{figure}

\subsubsection{User interaction} 
\label{sec:userinteraction}
We compared whether an intervention required users to \textit{actively} interact with the countermeasure (e.g., having to click to confirm sharing) or whether they could \textit{passively} ignore the countermeasure (e.g., a label below a post).
Further, some interventions cannot clearly be labeled as active or passive as the actual form of implementation is not (yet) defined (20 interventions in 18 publications). For instance, some approaches make a more generic proposal of a warning without stating if it can be ignored or not. Other approaches present an intervention which takes place only at the request of the user, for example as a separate smartphone app (e.g., \citep{barua_f-nad_2019}) and, thus, are neither active nor passive during the actual usage of social media. In cases, where the intervention is designed as a mandatory one-time experience (e.g., \citep{agley_intervening_2021, pennycook_fighting_2020, tsipursky_fighting_2018}), users do not have to interact with the intervention during the following usage of social media but definitely once before the usage. Thus, those interventions are classified as active.
We found that the majority of interventions are passive (111 interventions in 76 publications) while only 23 interventions (in 19 publications) identified publications deal with active interventions. A representation of the number of interventions regarding different interaction types can be found in Figure \ref{fig:activepassive}.

\begin{figure}
    \centering
    \includegraphics[width=\textwidth]{activepassiveAnzahlInterventionen.png}
    \caption{Number of interventions regarding each type of user interaction. Publications can contain multiple interventions with different user interaction types.}
    \label{fig:activepassive}
\end{figure}
    
\subsubsection{Time of intervention}
\label{sec:timeofintervention}
Misinformation interventions can address different points in time in the context of social media use. While countermeasures that take place long before the actual usage of social media (e.g., trainings, educational games for school lessons) were excluded from analysis, we included countermeasures that take place immediately before the exposure (e.g., short messages when logging in to a social media platform). We found that 24 interventions take place \textit{pre exposure} to misinformation (including 2 interventions coming with a combination of timings; 20 publications). For example, \citet{pennycook_fighting_2020} nudged participants of a large-scaled online study to think about accuracy before getting a news-sharing task. They asked participants to rate a headline's accuracy before exposing them to multiple other headlines and measuring their sharing intentions. Indeed, they found that giving a simple accuracy induction resulted in increased sharing discernment \citep{pennycook_fighting_2020}.
The majority of interventions (71 interventions in 53 publications) however, is designed to take place immediately \textit{during} the exposure to misinformation. This includes most of the corrections, warnings, labels and highlighting approaches. For example, \citet{bode_see_2018} compare algorithmic corrections via related articles from Snopes with social corrections via user comments underneath a Facebook post referring to the same debunking link on Snopes.

32 interventions in 27 publications deal with countermeasures \textit{post exposure}, e.g., \citet{grady_nevertheless_2021} compared warnings after exposure to an article with warnings during other points in time.
To specifically combat the spread of misinformation, a few interventions intervene directly \textit{at the moment of sharing} (6 interventions in publications; e.g., \citep{von_der_weth_nudging_2020}).
Detached from the actual social media platforms, some approaches offer their own platforms. Accordingly, the intervention here takes place \textit{on request of the user} at any time (14 interventions in 12 publications; e.g., \citep{barua_f-nad_2019}), when users have to actively reach out to the intervention on a separate platform.
A few interventions could not be assigned to one of those point in time (10 interventions in 8 publications). For example, \citet{furuta_fact-checking_2021} present a countermeasure to take place during article creation. 
While the majority of interventions specifically take place at one exact moment in time, four interventions are designed to take place at multiple points in time, combining for example a prebunking message with a warning during exposure to misinformation. 
See Figure \ref{fig:timing} for a visualization of the number of publications for each timing of the interventions.

\begin{figure}
    \centering
    \includegraphics[width=\textwidth]{timing_interventionsanzahl.png}
    \caption{Number of interventions addressing different points in time.}
    \label{fig:timing}
\end{figure}

\subsection{Which characteristics of misinformation interventions and their study design typically co-occur?}
\label{sec:coocc}
In this section, typical combinations of categories are highlighted. To identify interesting co-occurrency of methodological and content-wise categories, we plotted a correlation heatmap for exploratory analysis. Therefore, $Pearson$ correlation coefficients and according $p$-values ($\alpha=0.05$) were calculated. Figure \ref{fig:correlation_heatmap} highlights the identified significant correlations. When identifying interesting significant correlations, we took a closer look at the publications to confirm our findings with concrete examples.

\begin{figure}
\centering
    \includegraphics[width=\textwidth]{Rplot05.png}
    \caption{Correlation matrix between categories showing co-occurrencies (only for significant p-values; squares were left blank on non-significant coefficients ($\alpha=0.05$)). Abbreviations: meth = evaluation method; platf = platform; form = format; int = intervention design; time = timing of the intervention; impl = implementation.}
    \label{fig:correlation_heatmap}
\end{figure}

Some of our findings are to be expected: Many negative correlations occur closely to the diagonal axis as this is where neighboring subcategories are located. 
Further, some combinations of platform and format are to be expected: Interventions that complicate sharing take place at the moment of sharing and are typically classified as active, as the user is forced to interact with the intervention before sharing a misleading content.

Additionally, we found some interesting insights regarding co-occurrency:
First, we can see that there is a positive correlation between the addressed formats `video' and `image'. Indeed, three out of five interventions for video formats are specifically addressing images as well (e.g., \citep{axelsson_learning_2021}). 
Regarding intervention designs, it is often observed that either several different intervention designs are tested against each other \citep{hameleers_separating_2020} or that an intervention is presented which is composed of several types. For example, eight out of twelve interventions with visibility reduction display a warning as well - indicated by a significant positive correlation. Similarly, we can observe in the data that showing indicators for misinformation often comes with some sort of highlighting design or a specific visualization (of the indicators). For approaches that suggest a visibility reduction of misinformation, these interventions positively correlate with binary labels. For instance, \citet{kreps_covid-19_2022} evaluated false tags to COVID-19 misinformation, where the label as false information is placed as an overlay over the article and content is thus only visible to a limited extent. A similar intervention was evaluated by \citet{porter_political_2022}, both addressing the fact-checking label applied by Facebook.
While some intervention designs typically occur together, others tend to be mutually exclusive. For example, warnings negatively correlate with corrections and debunking. Indeed, there are many interventions that give a warning without exposing users to a correction or debunking of the misinformation (e.g., \citep{folkvord_effect_2022}). For corrections and debunking on the other hand, we can observe that these interventions are in most cases a stand-alone intervention design rather than being combined with indicators, binary labels or highlighting design. 
A visualization of intervention designs and their combinations on specific platforms is shown in Figure \ref{fig:interventiontypesplatforms}.

\begin{figure}
    \includegraphics[width=\textwidth]{interventiontype_platform.png}
    \caption{Visualization of intervention designs and common combinations on specific platforms. Instances smaller than 1 were excluded.}
    \label{fig:interventiontypesplatforms}
\end{figure}

When taking a closer look at when specific intervention designs take place, there are several interesting findings. For instance, warnings tend to occur immediately during the exposure to misinformation. Additionally, we can observe a negative correlation for warnings and `at request of the user' and `post exposure'. The same is true for interventions with visibility reduction, which is not surprising. For corrections and debunking, these intervention designs tend to take place during or post exposure to misleading contents. This can be explained by approaches that address corrective messages within the comment section of a social media post or studies that specifically evaluate the effect of corrections after participants were exposed to misinformation. For example, \citet{gesser-edelsburg_correcting_2018} investigated how correction by the ministry of health can affect vaccination intentions. A small number of interventions deals with prebunking, where a correction appears before the misinformation exposure (e.g., \citep{vraga_testing_2020}).
Another interesting insight concerning the timing of intervention designs is that showing indicators, binary labeling and specific visualizations tend to take place on request of the user. The positive correlation is not surprising when taking a closer look at the typical implementation of those interventions. Indeed, showing indicators, binary labeling, and specific visualizations positively correlate with implementations as own platforms which have to be actively reached out to on request of the user (e.g., \citep{barua_f-nad_2019}).



\subsection{How is transparency used to facilitate users autonomously dealing with misinformation?}
\label{sec:transparen}
As highlighted in Section \ref{sec:interventiontype}, digital countermeasures in the context of misinformation can have different objectives. While some interventions aim at reducing the spread of contents itself, others aim at communicating findings of the digital countermeasures to the end users, sometimes resulting in an environment that facilitates the strengthening of media literacy skills.
Within our systematic literature review, our special focus of interest is on transparent approaches that offer some form of explanation, as opposed to binary labels or deletion of problematic content.
Transparency can be achieved by different distinct intervention designs. One very common way of explanation is exposure to corrections or debunking. Indeed, the majority of publications within our scope deals with some sort of correction or debunking. While some approaches investigate the effect of user corrections within comment sections of social media posts, others focus on corrections of authorities. Interestingly, there is a very specific scientific discourse on the effectiveness of corrections, which is conducted in different disciplines. 
Corrections and debunking can be considered a very central part of combating misinformation online. This type of intervention provides an opportunity to thoroughly confront officially refuted content with facts. Often, this takes the form of a more detailed article, which backs up its corrections with official sources. Official fact-checking websites, which are linked from the intervention, are usually used for this purpose. 
On the other hand, there are approaches that aim for transparency through media literacy training, for example in the form of showing indicators and using a highlighting design of those (see examples in Figure \ref{fig:white-box-interventions}). Here it is examined which components of a social media post comprehensibly indicate that it is misleading content. We discussed this type of intervention in more detail in Section \ref{sec:interventiontype}. 

\begin{figure}[ht]
\centering
    \includegraphics[width=\textwidth]{TransparentExamples.png}
    \caption{Four exemplary interventions using transparent design to various degrees. A: An intervention showing the publish date of a video as indicator \citet{sherman_designing_2021}. B: An intervention highlighting parts of a text containing propaganda techniques \citet{martino_prta_2020}. C: An intervention used to affirm and refute claims using explainable machine learning \citet{ayoub_combat_2021}. D: The image comparison view as part of a larger system designed by \citet{karduni_vulnerable_2019}. The screenshots were taken from the respective papers.}
    \label{fig:white-box-interventions}
\end{figure}

\subsection{Nudging as an ambivalent trending countermeasure}
\label{sec:digitnudge}
During the content analysis of the identified relevant literature, one specific form of user intervention particularly caught the eye: 15 publications refer to their form of intervention as digital nudge. A nudge is defined as an intervention that \textit{``alters people’s behavior in a predictable way without significantly changing their economic incentives''} \citep[][p. 6]{thaler_richard_h_nudge_2009} and is a concept that has already been applied to many contexts such as cybersecurity and health. The concept of nudging is controversially discussed in research. Thus, under certain circumstances, it represents a potential for subconscious manipulation also in harmful directions. Like many digital countermeasures, digital nudges while promising, often offer little transparency and may run the risk of steering users in the wrong direction in ambiguous situations, if they are not actively engaged in critical thinking but rather trust blindly. For instance, \citet{lu_effects_2022} show that AI-based credibility indicators can be used to steer participants in a certain direction, even if the AI is wrong. 
Since we did not specifically review which of the 154 interventions within 108 publications actually fit the definition of a nudge, we would nevertheless like to provide an overview of the publications that refer to their interventions as nudges themselves.
Some publications present intervention as ``accuracy nudges'' and introduce concepts in which users are specifically nudged to reflect on the accuracy of content and to act more thoughtfully accordingly (e.g., \citep{aslett_news_2022,jia_understanding_2022,pennycook_fighting_2020,capraro_i_2022, nekmat_nudge_2020}). For example, \citet{capraro_i_2022} report promising results that indicate positive effects on sharing behavior when using an accuracy prompt. Similarly, \citet{von_der_weth_nudging_2020} developed `ShareAware' as a nudge for more conscious posting and sharing behavior. In a different approach, \citet{andi_nudging_2020} developed a social norm-based nudge to effect sharing behavior by exposing participants to the message: ``\textit{[...] Most responsible people think twice before sharing content with their friends and followers}''.
In contrast, there are attempts to nudge users not only away from misinformation \citep{duncan_whats_2020} but towards the consumption of credible news (e.g., \citep{horne_trustworthy_2019,gao_label_2018} or a habit of assessing accuracy of information \citep{jahanbakhsh_exploring_2021, bhuiyan_feedreflect_2018,bhuiyan_nudgecred_2021}. In that context, \citet{thornhill_digital_2019} developed a nudge to steer users into fact checking news online.

\section{Discussion}
\label{sec:disc}
In Section \ref{sec:res} we have systematically categorized a variety of existing misinformation interventions to assist in dealing with misinformation online, providing concrete examples of identified dimensions. 
The analysis of our systematic literature review underscores the impression that a variety of diverse approaches have emerged in recent years and continue to emerge. It can be observed that these often differ significantly in their characteristics. In this section we discuss and summarize our findings regarding our research questions.

\subsection{RQ1: What are typical methodological characteristics of existing studies on misinformation interventions?}
Methodologically, studies of misinformation interventions differ in various dimensions, although several emphases and typical patterns are also apparent. Due to the inclusion and exclusion criteria of our review, the sample contains mainly studies that collect empirical data and only a few publications with an exclusively conceptual approach. Typically, publications on misinformation interventions evaluate novel or already established interventions in user studies, often in comparison to other existing approaches. A particular focus is on web-based experiments with collection of quantitative and qualitative data as this method is suitable for large-scaled samples and a controlled environment.  
In order to examine the interventions in a realistic environment and to minimize biases, more evaluation in the form of field studies would be desirable for future studies.
It is striking that mainly U.S. adults and college students are surveyed as study participants, while specific (vulnerable) target groups such as teenagers, persons of older age or non-native speakers are largely neglected. This can be explained by the better accessibility of different user groups and represents a common problem known from other user studies in contexts of human-computer interaction and similar disciplines.

Not surprisingly, most publications deal with Facebook and Twitter as social media platforms or address news articles in general (see \ref{sec:methodologicaloverviews}). Considering current and emergent social media platforms like TikTok and Instagram as image- and video-based platforms is still largely missing within the research landscape. However, the impact of those platforms and content types has shown to be highly relevant. Looking closer at our publication sample, we can note that there are already isolated publications for addressing exceptional formats such as image, video and audio.

\subsection{RQ2: How do existing forms of user-centered misinformation interventions assist users in dealing with misinformation online?}
Looking closely at misinformation interventions, one notices a publication emphasis toward corrections and debunking. This form of intervention can be artificially controlled or occur naturally in the form of user comments. It is striking that corrections/debunking are examined in great detail in the literature from a wide variety of perspectives and with a focus on the smallest details. This finding is also supported by the review study by \citet{chen_spread_2022} who identified fact-based corrections as ``the most common type of corrective communication strategy'', classifying it as a part of message-based approaches.
In the classic case, corrections from the official side are based on thorough and elaborate journalistic work. For example, social media articles can only be linked to an official correction once the content has been thoroughly checked manually by experts. In the fast pace of social media and especially in emerging crisis situations, there is an overflow of accurate and misinformation that needs to be reacted to quickly. This is where corrections as digital countermeasures sometimes reach their limits as stand-alone interventions.
While this form of intervention provides users with additional (fact-based) knowledge and can thus create transparency, there are other types of intervention that aim to increase transparency and thus media competence, for example, through linguistic or content-related indicators. 
An advantage of those interventions is the scalability using automatic detection algorithms in real-time during emerging crisis situations and on large data sets, often based on machine learning approaches. However, when automatically showing indicators such as a missing verification seal or semantic propaganda techniques, the final decision on if a content is misinformation or not, either lies with the user or is taken over by the algorithm based on (potentially biased) training data, missing the expert knowledge of professional fact checkers.
Transparent misinformation interventions, independent of their implementation as correction or display of automatically detected indicators or other types, may offer the opportunity to counteract reactance of end users in contrast to approaches that lack an explanation and in some cases facilitate a feeling of censorship, paternalism, and loss of control.

In contrast to transparent approaches, there are also less educational interventions with the goal of reducing the consumption of misinformation through removal or visibility reduction. Both intervention designs can be considered helpful when taking into account the bias of people remembering contents itself without a potentially shown correction or warning when exposed to misleading content \citep{grady_nevertheless_2021}. On the other hand, this intervention design may lead to (a feeling of) censorship and a resulting migration to other platforms or tools that take less rigorous action against problematic content. While deleting/censoring dangerous or explicit content is a legitimate and important responsibility of social media platform operators, transferring this solution of deleting to all problematic content such as disinformation and misinformation would not only lead to the migration of users to less restrictive platforms. In particular, this would represent a wasted opportunity for media literacy education, which may in parts be achieved through transparent digital countermeasures as a supplement to teaching units in schools.

In order to develop misinformation interventions in a user-centered way and to be able to achieve an actual effect, the early inclusion of needs and requirements of different target groups is indispensable. A particular challenge is the accessibility of people who have no trust in official bodies. In this context, limits certainly emerge as to who can be reached at all by the corresponding technical tools. In order to avoid reactance, the timing of the intervention certainly plays a role in addition to the transparency of approaches.  In our systematic literature review, we identified interventions that can be used on request of the user and others that are permanently present during the normal use of social media. It is an interesting research question which point in time or which regularity of an intervention is suitable for which target groups.
Considering the broad variety of misinformation intervention, we hope to provide a helpful overview on existing forms. We propose our taxonomy as a starting point to systematically capture intervention categories and identify relevant dimensions. It is intended to provide researchers with a framework to develop new interventions, to pool knowledge from different disciplines for the promotion of cross-disciplinary research, and to reveal promising research directions.

\subsection{RQ3: Which trends and chances for future research can be derived from the existing literature?}
In this paper we have systematically analyzed 108 publications with 154 user interventions to assist in dealing with misinformation online. Our findings reveal current trends and movements in human-computer interaction, psychology, information systems, and communication sciences.
As potential avenues for future research we propose the following questions and interests:

\textbf{(1) Are approaches for specific platforms transferable to other new platforms?} 
With regard to particularly relevant contexts of use, it is also necessary to consider current and emergent social media platforms. Social media platforms are constantly changing. For some time now, there has been a noticeable trend toward TikTok, and Facebook in particular is losing a great deal of its importance, especially among younger people. In order not to have to reinvent the wheel again and again, studies on the transferability of findings to new types of platforms are important. While the majority of studies surveyed much-researched platforms such as Twitter and Facebook (e.g., \citep{bhuiyan_feedreflect_2018,kim_eye_2021,ozturk_combating_2015,mena_cleaning_2020}; see Figure \ref{fig:yearanzahl}
and Section \ref{sec:methodologicaloverviews}), there has been little research on misinformation on image- and video-based platforms such as Instagram and TikTok. Given the usage rates of these media, particularly among youth, and the increasing relevance of the platforms for misinformation (e.g., concerning the Ukraine war), addressing this research gap is considered particularly relevant. At the same time, there are major obstacles to overcome here, especially with regard to the availability of labeled datasets.

\textbf{(2) How can collected findings and technical approaches for text-based interventions be applied to emerging video- and image-based misinformation?} With regard to transferability to new platforms, transferability to other information channels is also particularly central. Can text-based user interventions (e.g., \citep{furuta_fact-checking_2021}) be adapted for video- and image-based channels? How can new indicators and measures for detecting misinformation (e.g., image reverse search) be integrated into misinformation interventions? 

\textbf{(3) How can chances of digital misinformation interventions be effectively combined with the advantages of human experts?} Fully automated mechanisms, e.g., for machine learning-based detection can handle large amounts of data better than humans. In contrast, trained humans as experts (e.g., journalists \citep{sotirakou_toward_2022}) can handle specific case decisions better than algorithms when the boundary between true and false is blurred and information is missing. How can human expert knowledge be used within digital countermeasures without losing the performance of the automatic tool? In which steps of the countermeasure can human intervention be integrated? 

\textbf{(4) How can automatic detection be combined with user-centered feedback?} Automatic detection is often a black-box procedure and therefore cannot explain its decision-making. Although there are already approaches to implement detection procedures as white-box approaches, their explainability is still highly complex at today's level and not very suitable, especially for people with low media literacy. How can the advantages of accurate automatic detection be combined with transparent and comprehensible explanations as user-centered feedback (see Section \ref{sec:transparen}; initial attempts e.g., by \citet{schmid_digital_2022})?

\textbf{(5) How can media literacy be encouraged without exposing users to misinformation?}
Approaches to increase media literacy are often based on a display of misinformation with additional reference to comprehensible indicators or debunking - in this case, the user is still exposed to the misinformation. Studies suggest that even with a simultaneous warning, the misinformation content may be remembered at a later point in time which speaks for less exposure to misinformation \citep{grady_nevertheless_2021}. However, users tend to feel reactance and paternalism when content is hidden or deleted \citep{kirchner_countering_2020}. How can media literacy be trained within misinformation interventions without continuing to expose users to misinformation?

\textbf{(6) Can vulnerable people profit from the general findings of participants with high media literacy? How can we reach vulnerable people with official tools?} The bias toward U.S. adults and college students as study participants continues to be striking. Since not all individuals are equally affected by misinformation \citep{shrestha_online_2020}, but rather particularly vulnerable groups exist, the inclusion of individuals with lower levels of media literacy in the iterative design and evaluation process of the interventions is essential. Initial approaches are already moving in this direction \citep{axelsson_learning_2021}. How can the findings be applied to vulnerable people (see Section \ref{sec:methodologicaloverviews}; e.g., older people \citep{sakhnini_review_2022}, children, teenagers \citep{axelsson_learning_2021}, non-native speakers)? How can these target groups be meaningfully integrated into the iterative design and evaluation process?

With our systematic literature review we hope to provide a starting point for cross-disciplinary debates and knowledge exchange, as well as an inspiration for future research.

\section{Limitations \& Conclusion}
\label{sec:limitationsconclusion}
The large amount of publications in the area of interest, including a variety of different disciplines, was a challenge to deal with. Therefore, we focused specifically on approaches that take place within the real-time usage of social media and excluded approaches (especially educational trainings, games, and presentations) that take place long before the actual usage of social media. However, those approaches may provide additional insights into effective and user-centered interventions and are therefore suggested as future research.
Within our work, we thoroughly categorized the publications regarding multiple characteristics, involving two researchers with expert knowledge on that field of research. As the publications provide information on our categories in varying detail, we cannot exclude the possibility that some interventions were classified differently than they were intended by the authors themselves.
Furthermore, when analyzing the variety of user interventions, efficacy of the approaches is of special interest. However, comparability of efficacy between individual approaches was limited due to the publication bias, where mostly studies with significant results are published (78 publications stated to present effective interventions, 8 present explicitly not effective interventions, and 26 publications did not evaluate effectiveness). Further, there are severely different approaches to tackle efficacy of a user intervention, resulting in a big challenge for research to compare results. 

Misinformation remains a threat to the democratic order and the cohesion of society and the fight against it remains important. It is a central goal to empower users in dealing with the overabundance of information online, and especially during emerging crises. Digital misinformation interventions are one of several starting points to address that challenge, complementing professional journalistic work and media literacy training at schools. In this work, we have given an overview of existing countermeasures and have developed a taxonomy in order to systematize misinformation interventions research.   
Finally, we hope that this work - being a first step towards the systematization of misinformation interventions research - serves as an inspiration for future research and facilitates cross-disciplinary exchange of knowledge.

\section{Ackknowledgements}
This work was supported by the German Federal Ministry for Education and Research (BMBF) in the project NEBULA (13N16361).


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