The Fake News Spreading Plague: Was it Preventable?

In 2010, a paper entitled "From Obscurity to Prominence in Minutes: Political Speech and Real-time search" won the Best Paper Prize of the Web Science 2010 Conference. Among its findings were the discovery and documentation of what was termed a "Twitter-bomb", an organized effort to spread misinformation about the democratic candidate Martha Coakley through anonymous Twitter accounts. In this paper, after summarizing the details of that event, we outline the recipe of how social networks are used to spread misinformation. One of the most important steps in such a recipe is the "infiltration" of a community of users who are already engaged in conversations about a topic, to use them as organic spreaders of misinformation in their extended subnetworks. Then, we take this misinformation spreading recipe and indicate how it was successfully used to spread fake news during the 2016 U.S. Presidential Election. The main differences between the scenarios are the use of Facebook instead of Twitter, and the respective motivations (in 2010: political influence; in 2016: financial benefit through online advertising). After situating these events in the broader context of exploiting the Web, we seize this opportunity to address limitations of the reach of research findings and to start a conversation about how communities of researchers can increase their impact on real-world societal issues

What does enrollment in a MOOC mean?

In 2012, when MOOCs became largely known, media reports were fascinated with the big number of enrollments. The number 150,000 students was mentioned for both Stanford’s Artificial Intelligence course and MIT’s Circuits and Electronics, to be later followed by the underwhelming completion rates, that often are in the single digit percentages. But what kind of enrollment do these large numbers really show? We try to answer this question by breaking this number into its components, while comparing two successive iterations of the same MOOC offered on the edX platform

Investigating Rumor Propagation with TwitterTrails

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 introduce {\sc TwitterTrails}, an interactive, web-based tool ({\tt 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 {\sc 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 rumor's level of visibility and, as an example of the power of crowdsourcing, the audience's skepticism towards it which correlates with the rumor's credibility. We envision {\sc TwitterTrails} as valuable tool for individual use, but we especially for amateur and professional journalists investigating recent and breaking stories. Further, its expanding collection of investigated rumors can be used to answer questions regarding the amount and success of misinformation on Twitter.Comment: 10 pages, 8 figures, under revie

The Rise and the Fall of a Citizen Reporter

Recently, research interest has been growing in the development of online communities sharing news and information curated by “citizen reporters”. Using “Big Data” techniques researchers try to discover influence groups and major events in the lives of such communities. However, the big picture may sometimes miss important stories that are essential to the development and evolution of online communities. In particular, how does one identify and verify events when the important actors are operating anonymously and without sufficient news coverage, as in drug war-torn Mexico? In this paper, we present some techniques that allow us to make sense of the data collected, identify important dates of significant events therein, and direct our limited resources to discover hidden stories that, in our case, affect the lives and safety of prominent citizen reporters. In particular, we describe how focused analysis enabled us to discover an important story in the life of this community involving the reputation of an anonymous leader, and how trust was built in order to verify the validity of that story

Sifting the Sand on the River Bank: Social Media as a Source for Research Data

Computational social science has been described as a new field at the intersection of computer science and social sciences, aiming to study the ways that society evolves, interacts, and reacts. Like prospectors sifting the sand in a river bed for gold, computational social science researchers are looking into the streams of social media for insight on our social interactions. Enabled by the availability of and easy accessibility to vast amounts of data generated by social entities, as well as by powerful computing hardware and algorithms, its researchers conduct observations of social interaction and experiments testing social theories in scales not realizable before. In this paper, after a short review of the characteristics of this new area, we discuss issues related to the types of data sought and used, and some of the challenges in collecting and interpreting the data. Throughout the paper we also examine some of the pitfalls awaiting and the standards that need to be observed

From Obscurity to Prominence in Minutes: Political Speech and Real-Time Search

Recently, all major search engines introduced a new feature: real-time search results, embedded in the first page of organic search results. The content appearing in these results is pulled by Twitter, blogs, and news websites within minutes of its generation. In this paper, we argue that in the context of political speech, this feature provides disproportionate exposure to personal opinions, fabricated content, unverified events, lies and misrepresentations that would otherwise not find their way in the first page, giving them the opportunity to spread virally. We provide evidence from the recent Massachusetts senate race between Martha Coakley and Scott Brown, analyzing attacks launched inside Twitter

DEMO: 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, webbased 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 rumor’s level of visibility and, as an example of the power of crowdsourcing, the audience’s skepticism towards it which correlates with the rumor’s credibility. We envision TWITTERTRAILS as valuable tool for individual use, and especially for amateur and professional journalists investigating recent and breaking stories

The power of prediction with social media

Social media provide an impressive amount of data about users and their interactions, thereby offering computer and social scientists, economists, and statisticians – among others – new opportunities for research. Arguably, one of the most interesting lines of work is that of predicting future events and developments from social media data. However, current work is fragmented and lacks of widely accepted evaluation approaches. Moreover, since the first techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Therefore, better understanding the predictive power and limitations of social media is of utmost importanc