Samantha Finn

Vocal Minority Versus Silent Majority: Discovering the Opionions of the Long Tail

Social networks such as Face book and Twitter have become the favorite places on the Web where people discuss real-time events. In fact, search engines such as Google and Bing have special agreements, which allow them to include into their search results public conversations happening in real-time in these social networks. However, for anyone who only reads these conversations occasionally, it is difficult to evaluate the (often) complex context in which these conversation bits are embedded. Who are the people carrying on the conversation? Are they random participants or people with a specific agenda? Making sense of real-time social streams often requires much more information than what is visible in the messages themselves. In this paper, we study this phenomenon in the context of one political event: a special election for the US Senate which took place in Massachusetts in January 2010, as observed in conversations on Twitter. We present results of data analysis that compares two groups of different users: the vocal minority (users who tweet very often) and the silent majority (users who tweeted only once). We discover that the content generated by these two groups is significantly different, therefore, researchers should take care in separating them when trying to create predictive models based on aggregated data.

Using TwitterTrails.com to Investigate Rumor Propagation

Social media have become part of modern news reporting, used by journalists to spread information and find sources, or as a news source by individuals. The quest for prominence and recognition on social media sites 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 demo TWITTERTRAILS, an interactive, web-based tool (twittertrails.com) that allows users to investigate the origin and propagation characteristics of a rumor and its refutation, if any, on Twitter. Visualizations of burst activity, propagation timeline, retweet and co-retweeted networks help its users trace the spread of a story. Within minutes TWITTERTRAILS will collect relevant tweets and automatically answer several important questions regarding a rumor: its originator, burst characteristics, propagators and main actors according to the audience. In addition, it will compute and report the rumors level of visibility and, as an example of the power of crowdsourcing, the audiences skepticism towards it which correlates with the rumors credibility. We envision TWITTERTRAILS as valuable tool for individual use, and especially for amateur and professional journalists investigating recent and breaking stories.

The Co-Retweeted Network and its Applications for Measuring the Perceived Political Polarization

This paper introduces a novel network, the co-retweeted network, that is constructed as the undirected weighted graph that connects highly visible accounts who have been retweeted by members of the audience during some real-time event. Like bibliographics co-citation used to indicate that two papers treat a related subject matter, co-retweeting is used to indicate that two accounts present similar opinions in an online discussion. Thus, the co-retweeted network can be seen as a form of consulting the opinion of the crowd that is following the discussion about the similarity (or difference) of positions expressed by the highly visible accounts. When applied on political conversations related to some event, the co-retweeted network enables the measurement of the polarity of political orientation of major players (including news organizations) based on the views of the audience. It can also measure the degree of polarization of the event itself.

Visualizing co-retweeting behavior for recommending relevant real-time content

Twitter is a popular medium for discussing unfolding events in real-time. Due to the large volume of user generated data during these events, its important to be able recommend the best content while its fresh. Current recommendation algorithms for Twitter take into account the users tweets and her social network, but since real-time events might be unique or unexpected, the history of a user may not be sufficient for finding the most relevant content. Additionally, for users who want to join the conversation at that specific moment (or follow it without having to create an account), the system will be faced with the cold-start problem. We propose a simple visualization technique that considers the activity of the whole community participating in the real-time discussion, by capturing their co-retweeting behavior. Such a technique depicts the big picture, allowing a user to choose content from parts of the community that share her opinions or beliefs.

Classroom Activity for Critical Analysis of News Propagation Online

We present an educational activity for college students to think critically about the truthfulness of news propagated in social media. This activity utilizes TwitterTrails, a visual tool to analyze Twitter claims, events, and memes. This tool provides views such as a propagation graph of a storys bursting activity, and the co-ReTweeted network of the more prominent members of the audience. Using a response and reflection form, students are guided through these different facets of a story. The classroom activity was iteratively designed over the course of three semesters. Here, we present the learning outcomes from our final semesters evaluation with 43 students. Our findings demonstrate that the activity provided students with both the conceptual tools and motivation to investigate the reliability of stories in social media. Our contribution also includes access to the tool and materials to conduct this activity. We hope that other educators will further improve and run this activity with their own students.