Tag Archives: Marathon

Analyzing Fake Content on Twitter During Boston Marathon Bombings

As iRevolution readers already know, the application of Information Forensics to social media is one of my primary areas of interest. So I’m always on the lookout for new and related studies, such as this one (PDF), which was just published by colleagues of mine in India. The study by Aditi Gupta et al. analyzes fake content shared on Twitter during the Boston Marathon Bombings earlier this year.

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Gupta et al. collected close to 8 million unique tweets posted by 3.7 million unique users between April 15-19th, 2013. The table below provides more details. The authors found that rumors and fake content comprised 29% of the content that went viral on Twitter, while 51% of the content constituted generic opinions and comments. The remaining 20% relayed true information. Interestingly, approximately 75% of fake tweets were propagated via mobile phone devices compared to true tweets which comprised 64% of tweets posted via mobiles.

Table1 Gupta et al

The authors also found that many users with high social reputation and verified accounts were responsible for spreading the bulk of the fake content posted to Twitter. Indeed, the study shows that fake content did not travel rapidly during the first hour after the bombing. Rumors and fake information only goes viral after Twitter users with large numbers of followers start propagating the fake content. To this end, “determining whether some information is true or fake, based on only factors based on high number of followers and verified accounts is not possible in the initial hours.”

Gupta et al. also identified close to 32,000 new Twitter accounts created between April 15-19 that also posted at least one tweet about the bombings. About 20% (6,073 accounts) of these new accounts were subsequently suspended by Twitter. The authors found that 98.7% of these suspended accounts did not include the word Boston in their names and usernames. They also note that some of these deleted accounts were “quite influential” during the Boston tragedy. The figure below depicts the number of suspended Twitter accounts created in the hours and days following the blast.

Figure 2 Gupta et al

The authors also carried out some basic social network analysis of the suspended Twitter accounts. First, they removed from the analysis all suspended accounts that did not interact with each other, which left just 69 accounts. Next, they analyzed the network typology of these 69 accounts, which produced four distinct graph structures: Single Link, Closed Community, Star Typology and Self-Loops. These are displayed in the figure below (click to enlarge).

Figure 3 Gupta et al

The two most interesting graphs are the Closed Community and Star Typology graphs—the second and third graphs in the figure above.

Closed Community: Users that retweet and mention each other, forming a closed community as indicated by the high closeness centrality values produced by the social network analysis. “All these nodes have similar usernames too, all usernames have the same prefix and only numbers in the suffixes are different. This indicates that either these profiles were created by same or similar minded people for posting common propaganda posts.” Gupta et al. analyzed the content posted by these users and found that all were “tweeting the same propaganda and hate filled tweet.”

Star Typology: Easily mistakable for the authentic “BostonMarathon” Twitter account, the fake account “BostonMarathons” created plenty of confusion. Many users propagated the fake content posted by the BostonMarathons account. As the authors note, “Impersonation or creating fake profiles is a crime that results in identity theft and is punishable by law in many countries.”

The automatic detection of these network structures on Twitter may enable us to detect and counter fake content in the future. In the meantime, my colleagues and I at QCRI are collaborating with Aditi Gupta et al. to develop a “Credibility Plugin” for Twitter based on this analysis and earlier peer-reviewed research carried out by my colleague ChaTo. Stay tuned for updates.

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See also:

  • Boston Bombings: Analyzing First 1,000 Seconds on Twitter [link]
  • Taking the Pulse of the Boston Bombings on Twitter [link]
  • Predicting the Credibility of Disaster Tweets Automatically [link]
  • Auto-Ranking Credibility of Tweets During Major Events [link]
  • Auto-Identifying Fake Images on Twitter During Disasters [link]
  • How to Verify Crowdsourced Information from Social Media [link]
  • Crowdsourcing Critical Thinking to Verify Social Media [link]

Taking the Pulse of the Boston Marathon Bombings on Twitter

Social media networks are evolving a new nervous system for our planet. These real-time networks provide immediate feedback loops when media-rich societies experience a shock. My colleague Todd Mostak recently shared the tweet map below with me which depicts tweets referring to “marathon” (in red) shortly after the bombs went off during Boston’s marathon. The green dots represent all the other tweets posted at the time. Click on the map to enlarge. (It is always difficult to write about data visualizations of violent events because they don’t capture the human suffering, thus seemingly minimizing the tragic events).

Credit: Todd Mostak

Visualizing a social system at this scale gives a sense that we’re looking at a living, breathing organism, one that has just been wounded. This impression is even more stark in the dynamic visualization captured in the video below.

This an excerpt of Todd’s longer video, available here. Note that this data visualization uses less than 3% of all posted tweets because 97%+ of tweets are not geo-tagged. So we’re not even seeing the full nervous system in action. For more analysis of tweets during the marathon, see this blog post entitled “Boston Marathon Explosions: Analyzing First 1,000 Seconds on Twitter.”

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Boston Marathon Explosions: Analyzing First 1,000 Seconds on Twitter

My colleagues Rumi Chunara and John Brownstein recently published a short co-authored study entitled “Twitter as a Sentinel in Emergency Situations: Lessons from the Boston Marathon Explosions.” At 2.49pm EDT on April 15, two improvised bombs exploded near the finish line of the 117th Boston Marathon. Ambulances left the scene approximately 9 minutes later just as public health authorities alerted regional emergency departments of the incident.

Meanwhile, on Twitter:

BostonTweets

An analysis of tweets posted within a 35 mile radius of the finish line reveals that the word stems containing “explos*” and “explod*” appeared on Twitter just 3 minutes after the explosions. “While an increase in messages indicating an emergency from a particular location may not make it possible to fully ascertain the circumstances of an incident without computational or human review, analysis of such data could help public safety officers better understand the location or specifics of explosions or other emergencies.”

In terms of geographical coverage, many of the tweets posted during the first 10 minutes were from witnesses in the immediate vicinity of the finish line. “Because of their proximity to the event and content of their postings, these individuals might be witnesses to the bombings or be of close enough proximity to provide helpful information. These finely detailed geographic data can be used to localize and characterize events assisting emergency response in decision-making.”

BostonBombing2

Ambulances were already on site for the marathon. This is rarely the case for the majority of crises, however. In those more common situations, “crowdsourced information may uniquely provide extremely timely initial recognition of an event and specific clues as to what events may be unfolding.” Of course, user-generated content is not always accurate. Filtering and analyzing this content in real-time is the first step in the verification process, hence the importance of advanced computing. More on this here.

“Additionally, by comparing newly observed data against temporally adjusted keyword frequencies, it is possible to identify aberrant spikes in keyword use. The inclusion of geographical data allows these spikes to be geographically adjusted, as well. Prospective data collection could also harness larger and other streams of crowdsourced data, and use more comprehensive emergency-related keywords and language processing to increase the sensitivity of this data source.” Furthermore, “the analysis of multiple keywords could further improve these prior probabilities by reducing the impact of single false positive keywords derived from benign events.”

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Self-Organized Crisis Response to #BostonMarathon Attack

I’m going to keep this blog post technical because the emotions from yesterday’s events are still too difficult to deal with. Within an hour of the bombs going off, I received several emails asking me to comment on the use of social media in Boston and how it differed to the digital humanitarian response efforts I am typically engaged in. So here are just a few notes, nothing too polished, but some initial reactions.

I Stand with Boston

Once again, we saw the outpouring of operational support from the “Crowd” with over two thousand people in the Boston area volunteering to take people in if they needed help, and this within 60 minutes of the attack. This was coordinated via a Google Spreadsheet & Google Form. This is not the first time that these web-based solutions were used for disaster response. For example, Google Spreadsheets was used to coordinate grassroots response efforts during the major Philippine floods in 2012.

We’re not all affected the same way during a crisis and those of us who are less affected almost always look for ways to help. Unlike the era of television broadcasting, the crowd can now become an operational actor in disaster response. To be sure, paid disaster response professionals cannot be everywhere at the same time, but the crowd is always there. This explains I have look called for a “Match.com for disaster response” to match local needs with local resources. So while I received numerous pings on Twitter, Skype and email about launching a crisis map for Boston, I am skeptical that doing so would have added much value.

What was/is needed is real-time filtering of social media content and matching of local needs (information and material needs) with local resources. There are two complementary ways to do this: human computing (e.g., crowdsourcing, microtasking, etc) and machine computing (natural language processing, machine learning, etc), which is why my team and I at QCRI are working on developing these solutions.

Other observations from the response to yesterday’s tragedy:

  • Boston Police made active use of their Twitter account to inform and advise. They also asked other Twitter users to spread their request for everyone to leave the city center area. The police and other emergency services also actively crowdsourced photographs and video footage to begin their criminal investigations. There was such heavy multimedia social media activity in the area that one could no doubt develop a Photosynth rendering of the scene.
  • There were calls for residents to unlock their Wifi networks to enable people in the streets to get access to the Internet. This was especially important after the cellphone network was taken offline for security reasons. To be sure, access to information is equally important as access to water, food, shelter, etc, during a crisis.

I’d welcome any other observation from readers, e.g., similarities and differences between the use of technologies for domestic emergency management versus international humanitarian efforts. I would also be interested to hear thoughts about how the two could be integrated or at the very least learn from each other.

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