Who Will Retweet This? Automatically Identifying and Engaging Strangers on Twitter to Spread Information

There has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame. To address this problem, we have developed two models: (i) a feature-based model that leverages peoples' exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to propagate information on Twitter via the act of retweeting; and (ii) a wait-time model based on a user's previous retweeting wait times to predict her next retweeting time when asked. Based on these two models, we build a recommender system that predicts the likelihood of a stranger to retweet information when asked, within a specific time window, and recommends the top-N qualified strangers to engage with. Our experiments, including live studies in the real world, demonstrate the effectiveness of our work

Who let the trolls out? Towards understanding state-sponsored trolls

Recent evidence has emerged linking coordinated campaigns by state-sponsored actors to manipulate public opinion on the Web. Campaigns revolving around major political events are enacted via mission-focused ?trolls." While trolls are involved in spreading disinformation on social media, there is little understanding of how they operate, what type of content they disseminate, how their strategies evolve over time, and how they influence the Web's in- formation ecosystem. In this paper, we begin to address this gap by analyzing 10M posts by 5.5K Twitter and Reddit users identified as Russian and Iranian state-sponsored trolls. We compare the behavior of each group of state-sponsored trolls with a focus on how their strategies change over time, the different campaigns they embark on, and differences between the trolls operated by Russia and Iran. Among other things, we find: 1) that Russian trolls were pro-Trump while Iranian trolls were anti-Trump; 2) evidence that campaigns undertaken by such actors are influenced by real-world events; and 3) that the behavior of such actors is not consistent over time, hence detection is not straightforward. Using Hawkes Processes, we quantify the influence these accounts have on pushing URLs on four platforms: Twitter, Reddit, 4chan's Politically Incorrect board (/pol/), and Gab. In general, Russian trolls were more influential and efficient in pushing URLs to all the other platforms with the exception of /pol/ where Iranians were more influential. Finally, we release our source code to ensure the reproducibility of our results and to encourage other researchers to work on understanding other emerging kinds of state-sponsored troll accounts on Twitter.https://arxiv.org/pdf/1811.03130.pdfAccepted manuscrip

Using Social Media to Promote STEM Education: Matching College Students with Role Models

STEM (Science, Technology, Engineering, and Mathematics) fields have become increasingly central to U.S. economic competitiveness and growth. The shortage in the STEM workforce has brought promoting STEM education upfront. The rapid growth of social media usage provides a unique opportunity to predict users' real-life identities and interests from online texts and photos. In this paper, we propose an innovative approach by leveraging social media to promote STEM education: matching Twitter college student users with diverse LinkedIn STEM professionals using a ranking algorithm based on the similarities of their demographics and interests. We share the belief that increasing STEM presence in the form of introducing career role models who share similar interests and demographics will inspire students to develop interests in STEM related fields and emulate their models. Our evaluation on 2,000 real college students demonstrated the accuracy of our ranking algorithm. We also design a novel implementation that recommends matched role models to the students.Comment: 16 pages, 8 figures, accepted by ECML/PKDD 2016, Industrial Trac

Facts and Fabrications about Ebola: A Twitter Based Study

Microblogging websites like Twitter have been shown to be immensely useful for spreading information on a global scale within seconds. The detrimental effect, however, of such platforms is that misinformation and rumors are also as likely to spread on the network as credible, verified information. From a public health standpoint, the spread of misinformation creates unnecessary panic for the public. We recently witnessed several such scenarios during the outbreak of Ebola in 2014 [14, 1]. In order to effectively counter the medical misinformation in a timely manner, our goal here is to study the nature of such misinformation and rumors in the United States during fall 2014 when a handful of Ebola cases were confirmed in North America. It is a well known convention on Twitter to use hashtags to give context to a Twitter message (a tweet). In this study, we collected approximately 47M tweets from the Twitter streaming API related to Ebola. Based on hashtags, we propose a method to classify the tweets into two sets: credible and speculative. We analyze these two sets and study how they differ in terms of a number of features extracted from the Twitter API. In conclusion, we infer several interesting differences between the two sets. We outline further potential directions to using this material for monitoring and separating speculative tweets from credible ones, to enable improved public health information.Comment: Appears in SIGKDD BigCHat Workshop 201

Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls