Category Archives: About TwitterTrails

Blog posts about the TwitterTrails System: how to use it, what the different visualizations and graphs show, and how the algorithms work.

False and True rumors look very differently on TwitterTrails

About TwitterTrails, Case Studies, False Claims, True Claims

News of Turkish airplane shooting down a Russian one over the Turkish-Syrian border has dominated the news and the social media lately. We investigated the rumor within hours after it appeared (24 Nov. 2015) and you can see the ressults of the analysis here: http://twittertrails.wellesley.edu/~trails/stories/investigate.php?id=462776628

This was not the first time a rumor of this kind emerged. About a month and a half ago (10 Oct. 2015) an identical rumor had emerged. We had investigated that rumor too and you can see the results of our anaysis here: http://twittertrails.wellesley.edu/~trails/stories/investigate.php?id=134661966

Russian jet downing rumors

As you can see, based on the crowd’s reaction to the rumors, TwitterTrails was able to determine that the October rumor was false while the November one was true. The false rumor did not spread much and had a lot of skeptical tweets questioning its validity. On the other hand, the true rumor spread much higher and in terms of skepticism was undisputed.

Our understanding of the way the “wisdom of the crowd” works is that, when unbiased, emotionally cool observers see a rumor that seems suspicious, they usually react in one of two ways: They either do not retweet it, reducing its spread, or they may respond questioning the validity of the rumor, resulting in higher skepticism.

Continue reading False and True rumors look very differently on TwitterTrails →

Charting Skepticism vs. Spread and determining whether claims are likely true or false

About TwitterTrails

If you’ve read our blog post False rumors do not spread like true ones you will probably recognize this graph:

graph

It plots the skepticism of TwitterTrails stories vs. their spread, contrasting how much doubt people have in the information a story presents and how visible and discussed a story was (read more about Spread and Skepticism here).   This graph is now visible to the public, and you can view the skepticism and spread of all the stories in our database: RUMOR SPREAD vs SKEPTICISM graph (hovering over a point displays skepticism and spread, and clicking brings up a summary of the story)

Continue reading for more information about this graph, and how TwitterTrails determines whether claims are likely true or false based on the wisdom of the crowd on Twitter.

Continue reading Charting Skepticism vs. Spread and determining whether claims are likely true or false →

TwitterTrails Metrics: Spread and Skepticism

About TwitterTrails

Spread and Skepticism are the two metrics used by TwitterTrails to gauge the impact of a story on Twitter, and Twitter’s reaction to the validity or truthfulness of the story. This posting gives information of what these metrics are and examples of their usage.

In this post we discuss the spread and skepticism of three claims, all of which are publicly viewable on TwitterTrails: the claim that people on welfare will receive free cars, the claim that the wife of the police chief in Ferguson wrote a racist post on her Facebook account, and the claim that Robin Williams died on August 11th, 2014.

three claims

Continue reading TwitterTrails Metrics: Spread and Skepticism →

False rumors do not spread like True ones

About TwitterTrails

On Twitter, claims that receive higher skepticism and lower spread scores are more likely to be false.
On the other hand, claims that receive lower skepticism and higher spread scores are more likely to be true.

The above is a conjecture we wrote in a recent paper entitled Investigating Rumor Propagation with TwitterTrails (currently under review). Feel free to take a look if you want to know more details about our system, but we will write here some of its highlights.

As you may know if you have read our Twitter Trails Blog before, we are developing a Web service that, starting from a tweet or a set of keywords related to a story spreading on Twitter (or a hashtag), it will investigate it and answer automatically some of the basic questions regarding the story. If you are not familiar, you may want to take a look at some of the posts. Or, it can wait until you read this one.

Recently we deployed twittertrails.com a site containing the growing collection of stories and rumors that we investigate. Its front end looks like this:

condensed_view_v2

 

This is the “condensed view” which allocates one line per story, 20 stories per page. There are over 120 stories collected at this point. Clicking on a title brings you the investigation page with lots of details and visualizations about its spread, its originator, how it burst, who supports it and who refutes it.

Continue reading False rumors do not spread like True ones →

Welcome to the TRAILS Blog!

About TwitterTrails, Introduction

Welcome! Glad you found us!

This blog is written by the TRAILS research team at Wellesley. Its main purpose is to demonstrate the use of the TRAILS system that is tracking the ways that tweets propagate.

Why did we built TRAILS? Glad you asked!

Social media have become part of modern news reporting, whether it is being used by journalists to spread information and find sources, or as a medium by citizen reporters. The quest for prominence and recognition on websites like Twitter can sometimes eclipse accuracy and lead to the spread of false information. As a way to study and react to this trend, we introduce TRAILS, an interactive, web-based tool that allows users to investigate the origin and propagation characteristics of a rumor and its denial, if any, on Twitter.

The TRAILS system is composed of a collection of data analysis and visualization tools and provide answers to several questions related to propagation of a rumor on Twitter, including:

  • Originator: Who posted the information first?
  • Burst: When and how did the story break?
  • Timeline: Is the story still spreading at the time of the inquiry?
  • Propagators: Who has been retweeting and spreading the story, given the retweets often indicate agreement?
  • Negation: Were there any related denying stories competing for attention?
  • Main actors: Who were the main actors in the propagation, according to the Twitter audience?

While we envision that TRAILS would be valuable as a tool for individual use, in the initial stages we see it as a tool for amateur and professional journalists investigating recent and breaking stories.

Next, please take a look at individual rumors we have analyzed. We aim to use these initial stories to help people understand how TRAILS work.

Are you ready to explore Trails yourself?  

If you have any questions regarding the TRAILS project, please contact pmetaxas@wellesley.edu.