Sentiment analysis during Hurricane Sandy in emergency response

Sentiment analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of users about different aspects of products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users during disaster events. Identifying such sentiments from online social networking sites can help emergency responders understand the dynamics of the network, e.g., the main users' concerns, panics, and the emotional impacts of interactions among members. In this paper, we perform a sentiment analysis of tweets posted on Twitter during the disastrous Hurricane Sandy and visualize online users' sentiments on a geographical map centered around the hurricane. We show how users' sentiments change according not only to their locations, but also based on the distance from the disaster. In addition, we study how the divergence of sentiments in a tweet posted during the hurricane affects the tweet retweetability. We find that extracting sentiments during a disaster may help emergency responders develop stronger situational awareness of the disaster zone itself

Values and Self-Presentation in Online Communication by Stakeholders Related to Homelessness

Values are guiding principles of what we consider important in our lives. They shape, and are shaped by, our information behaviors and interactions with technology. Design approaches that explicitly consider values can change the affordances of resulting technologies. This dissertation extends research related to values and information technology use and design within the social context of homelessness, a value-laden social issue in the United States. This study used both quantitative and qualitative content analysis to examine the values expressed in online communication (specifically, the 140-character posts on Twitter known as "tweets") by individuals who identified as homeless in their Twitter profiles. They were compared to the values expressed in the tweets of other stakeholders related to the issue of homelessness, including support organizations and homeless advocates, as well as a comparison group of individuals who did not identify with homelessness in their Twitter profiles. A key contribution of this study is an empirically tested coding manual for identifying salient values of Twitter users through their tweets. The application of this coding manual to Twitter users' timelines of tweets helped to characterize the ways in which values emerge from online communication, highlighting differences between the values expressed by individuals and organizations on Twitter. The study also showed how Twitter users' self-presentation of their online profiles relates to their expressions of values. These findings show how the role of values in one's self-presentation online leads to important implications for the design of sociotechnical systems and for raising awareness about the intersection of technology use and homelessness in the 21st century. These insights are necessary for understanding information technology use by individuals who are relevant but often absent from the development of new information technologies

Bots increase exposure to negative and inflammatory content in online social systems

Societies are complex systems which tend to polarize into sub-groups of individuals with dramatically opposite perspectives. This phenomenon is reflected -- and often amplified -- in online social networks where, however, humans are no more the only players, and co-exist alongside with social bots, i.e., software-controlled accounts. Analyzing large-scale social data collected during the Catalan referendum for independence on October 1, 2017, consisting of nearly 4 millions Twitter posts generated by almost 1 million users, we identify the two polarized groups of Independentists and Constitutionalists and quantify the structural and emotional roles played by social bots. We show that bots act from peripheral areas of the social system to target influential humans of both groups, bombarding Independentists with violent contents, increasing their exposure to negative and inflammatory narratives and exacerbating social conflict online. Our findings stress the importance of developing countermeasures to unmask these forms of automated social manipulation.Comment: 8 pages, 5 figure

Influence of augmented humans in online interactions during voting events

The advent of the digital era provided a fertile ground for the development of virtual societies, complex systems influencing real-world dynamics. Understanding online human behavior and its relevance beyond the digital boundaries is still an open challenge. Here we show that online social interactions during a massive voting event can be used to build an accurate map of real-world political parties and electoral ranks. We provide evidence that information flow and collective attention are often driven by a special class of highly influential users, that we name "augmented humans", who exploit thousands of automated agents, also known as bots, for enhancing their online influence. We show that augmented humans generate deep information cascades, to the same extent of news media and other broadcasters, while they uniformly infiltrate across the full range of identified groups. Digital augmentation represents the cyber-physical counterpart of the human desire to acquire power within social systems.Comment: 11 page

White, Man, and Highly Followed: Gender and Race Inequalities in Twitter

Social media is considered a democratic space in which people connect and interact with each other regardless of their gender, race, or any other demographic factor. Despite numerous efforts that explore demographic factors in social media, it is still unclear whether social media perpetuates old inequalities from the offline world. In this paper, we attempt to identify gender and race of Twitter users located in U.S. using advanced image processing algorithms from Face++. Then, we investigate how different demographic groups (i.e. male/female, Asian/Black/White) connect with other. We quantify to what extent one group follow and interact with each other and the extent to which these connections and interactions reflect in inequalities in Twitter. Our analysis shows that users identified as White and male tend to attain higher positions in Twitter, in terms of the number of followers and number of times in user's lists. We hope our effort can stimulate the development of new theories of demographic information in the online space.Comment: In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI'17). Leipzig, Germany. August 201

Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections

Adversarial interactions against politicians on social media such as Twitter have significant impact on society. In particular they disrupt substantive political discussions online, and may discourage people from seeking public office. In this study, we measure the adversarial interactions against candidates for the US House of Representatives during the run-up to the 2018 US general election. We gather a new dataset consisting of 1.7 million tweets involving candidates, one of the largest corpora focusing on political discourse. We then develop a new technique for detecting tweets with toxic content that are directed at any specific candidate.Such technique allows us to more accurately quantify adversarial interactions towards political candidates. Further, we introduce an algorithm to induce candidate-specific adversarial terms to capture more nuanced adversarial interactions that previous techniques may not consider toxic. Finally, we use these techniques to outline the breadth of adversarial interactions seen in the election, including offensive name-calling, threats of violence, posting discrediting information, attacks on identity, and adversarial message repetition

A Systematic Identification and Analysis of Scientists on Twitter

Metrics derived from Twitter and other social media---often referred to as altmetrics---are increasingly used to estimate the broader social impacts of scholarship. Such efforts, however, may produce highly misleading results, as the entities that participate in conversations about science on these platforms are largely unknown. For instance, if altmetric activities are generated mainly by scientists, does it really capture broader social impacts of science? Here we present a systematic approach to identifying and analyzing scientists on Twitter. Our method can identify scientists across many disciplines, without relying on external bibliographic data, and be easily adapted to identify other stakeholder groups in science. We investigate the demographics, sharing behaviors, and interconnectivity of the identified scientists. We find that Twitter has been employed by scholars across the disciplinary spectrum, with an over-representation of social and computer and information scientists; under-representation of mathematical, physical, and life scientists; and a better representation of women compared to scholarly publishing. Analysis of the sharing of URLs reveals a distinct imprint of scholarly sites, yet only a small fraction of shared URLs are science-related. We find an assortative mixing with respect to disciplines in the networks between scientists, suggesting the maintenance of disciplinary walls in social media. Our work contributes to the literature both methodologically and conceptually---we provide new methods for disambiguating and identifying particular actors on social media and describing the behaviors of scientists, thus providing foundational information for the construction and use of indicators on the basis of social media metrics