Facebook Trends Data A Facebook trend is essentially a news event, which may be linked to several links/posts featuring the news story. Every trend comes with a headline (explaining the trend), related words (these are Named Entities and exists as Facebook openGraph nodes), a rank (indicating its popularity in the Facebook world) and the geographical zone the data was sampled from. Facebook currently provides trends across five zones: USA, Canada, Great Britain, India and Australia. Trends for each location are ranked from 0–49 in decreasing order of trending nature/popularity. Online news media has rapidly transformed into a mobile, real-time phenomenon. Notably, Twitter trends was a powerful evolution in the domain of breaking news. However, the nature of Facebook trends is somewhat different from Twitter. Unlike the microblogging site, each trend in Facebook is not necessarily a multi-word or a hashtag. Instead, a Facebook trend is an event headline with related media (links). Facebook uses several natural language processing (NLP) algorithms that automate the task of attaching related media, topic extraction, summarization and headline generation for a link. Parsing natural language is quick, but not always realtime. Thus, Facebook trends are slower to surface than Twitter. On the other hand, Facebook trends are richer in interpretability than Twitter because of included topic summarization, headlines and openGraph named entities. Lets first look at how each geographical zone got initially exposed to the news. I’ll then explain how the factors led to this unusual adoption path. ‘Hong Kong’ News Diffusion on Facebook I first plotted a persistence chart for the ‘Hong Kong’ trend on Facebook across the 5 geographical zones. A dot indicates the trend occurred in the geographical location at some particular time. Gaps indicate it fell out of the top 10 trending list. https://twitter.com/_RoySD/status/516706217800908800 Observe that the trend originated in Australia. It then started trending in India approximately 8 hours later, followed by Canada, GB in quick succession and finally made its way to the US. What drives this special geographical route of acceptance, continuation and departure from attention on Facebook? To answer that question, we must comprehend what the driving factors behind trend-making on Facebook are. Driving Factors in trend-making To find the driving factors that make news stories into trends, we sample Facebook data every 5 minutes. Facebook provides us with at most 50 news stories that are trending at some time. On the newsfeed page, users can see only 10 of the most popular news stories (not all 50). What happens to the other 40 or so stories? These swim below the surface, competing with each other, trying to break into the visible top-10 list. I track five events that unfolded over the first two days: (A) Protesters clash with police, (B) Thousands of activists occupy Hong Kong financial center (C) Police fire tear gas, (D) Police reduce force after 47 people are injured, and (E) Protesters begin stockpiling supplies. I found three key factors that influences attention on a news story and significantly decides it trending fate. Luckily for us, the impact of all these factors is quantifiable. For my analysis and visualizations, I re-scale the ranking list from 0:49 to 9:-40. In other words, the highest ranked news story (most popular one) now has a score =9. The 10th most popular news story (and the last one to make the trend list/ visible to users) has a score =0. (1) Time of Day The time of the day when the story breaks is important. People don’t share when they are sleeping (at least we hope not). Diurnal patterns are common in social media, and there is no exception in Facebook. A piece of news that breaks late in the evening has a lesser chance of sustaining as a trend. There remains a possibility such a news piece might be picked up in the next morning though.

Diurnal Patterns of trending popularity of news stories in Canada (green) and Great Britain (purple). Notice how stories E and F do not gain traction in GB as evening and night approaches. On the other hand, the same stories, breaking in almost identical times in Canada will sustain the trend for the next 7.5 hours, until Canada itself goes to sleep and the trend falls off. Also notice after 5 AM, the trend rises in GB, and eventually takes a long time to re-enter the top-10 and visible section of the trending list. [INTERACTIVE]

(2) Competing News Stories Second, the number of competing news trends in a geographical community affects the trend sustainability. Competing stories reflects the ecological conflict that a piece of news faces to break into the top-10 list and maintain its spot. Based on the number of competing news stories in a zone, we can calculate the Likelihood of trends making it into the the top-10 list. The lower the likelihood in a region, the higher the chances of the news story sustaining itself as a top-10 trend.

Competing News stories offer lesser likelihood that any particular news will be able to make an appearance in the top-10 trending list. This likelihood is unbalanced depending strongly on the geographical region. For example, the likelihood of a news story making it as a Facebook trend is nearly twice as high in Australia compared to the USA. [INTERACTIVE visualization]

The likelihood of a news story entering the top-10 list is remarkably varied for different geographical zones. It is highest for Australia at 0.71 on average, followed by India (avg. 0.51), Canada (avg. 0.48), GB (avg. 0.46) and USA at (avg. 0.40). Its interesting to notice that the lower the likelihood value, the more choppy the trend line appears to be. Notice that this sequence of decreasing likelihood is identical to the geographical progression of the ‘Hong Kong’ news diffusion. (3) The Escape Velocity Finally, how high the story rises in the trending list after it breaks and how long it lasts there plays a crucial role in the story’s eventual longevity. Lets track news about events in Hong Kong that break almost at the same time in two geographical locations. For the same story at breaking points [E] and [F] in Australia and India respectively, notice the former trend sustains while the latter does not. For another story at breaking point [G] and [H], the former trend which was among the top-3 sustains whereas the latter fails to sustain in the next 2 hours. The trend in Australia reaches a top-3 slot in both occasions, and ends up sustaining for ~16 hours. The trend in India falls of the top-10 list quickly after it breaks.

A trend that stays within the top-3 in the trending list for at least an hour and half seems to gain momentum and can sustain for longer periods as a top-10 trend. This is clear for the first two stories that break at identical times in Australia and India. [INTERACTIVE visualization with timeline in EDT]

I observe similar phenomenon in other geolocations. Consider the same news story (Police firing tear gas) which breaks at the same time in Canada and India as [D] and [C] respectively (shown below). The trend in Canada starts in the top-3, and continues to trend for the next 23.5 hours. In India the trend starts as the 7th-most popular. It drops out of the trending list after just 1.5 hours.

The trend is sustained in Canada while it dies off in India after the event story breaks. The news trends within the top-3 in Canada while it is does not in India. The exposure received by the top-3 spot drastically affects the sustaining period, even though Canada has more competing stories and a lower likelihood of trends entering the top-10 list than India. [INTERACTIVE visualization with timeline in EDT]