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.

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.