Modeling the formation of attentive publics in social media: the case of Donald Trump

Previous research has shown the importance of Donald Trump’s Twitter activity, and that of his Twitter following, in spreading his message during the primary and general election campaigns of 2015–2016. However, we know little about how the publics who followed Trump and amplified his messages took shape. We take this case as an opportunity to theorize and test questions about the assembly of what we call “attentive publics” in social media. We situate our study in the context of current discussions of audience formation, attention flow, and hybridity in the United States’ political media system. From this we derive propositions concerning how attentive publics aggregate around a particular object, in this case Trump himself, which we test using time series modeling. We also present an exploration of the possible role of automated accounts in these processes. Our results reiterate the media hybridity described by others, while emphasizing the importance of news media coverage in building social media attentive publics.Accepted manuscrip

Exploring Temporal Patterns in Classifying Frustrated and Delighted Smiles

We create two experimental situations to elicit two affective states: frustration, and delight. In the first experiment, participants were asked to recall situations while expressing either delight or frustration, while the second experiment tried to elicit these states naturally through a frustrating experience and through a delightful video. There were two significant differences in the nature of the acted versus natural occurrences of expressions. First, the acted instances were much easier for the computer to classify. Second, in 90 percent of the acted cases, participants did not smile when frustrated, whereas in 90 percent of the natural cases, participants smiled during the frustrating interaction, despite self-reporting significant frustration with the experience. As a follow up study, we develop an automated system to distinguish between naturally occurring spontaneous smiles under frustrating and delightful stimuli by exploring their temporal patterns given video of both. We extracted local and global features related to human smile dynamics. Next, we evaluated and compared two variants of Support Vector Machine (SVM), Hidden Markov Models (HMM), and Hidden-state Conditional Random Fields (HCRF) for binary classification. While human classification of the smile videos under frustrating stimuli was below chance, an accuracy of 92 percent distinguishing smiles under frustrating and delighted stimuli was obtained using a dynamic SVM classifier.MIT Media Lab ConsortiumProcter & Gamble Compan

Performing “digital labor bayanihan”: strategies of influence and survival in the platform economy

Drawing from experience of platform labor in one of the largest labor supplying countries, the Philippines, the paper demonstrates the role of an emerging labor category – that of digital labor influencers – who promote the viability of platform labor locally amid its precarious and ambiguous conditions. Through participant observation in Facebook groups, analysis of YouTube channels and videos, and interviews with digital labor influencers and workers, we present insights into the interventions that these influencers use, anchoring their strategies on what we call performing “digital labor bayanihan”: (a) coaching workers on the “possibilities” of the platform economy and on how to navigate its structural ambiguities, (b) by acting as “agencies”, they aid workers to span boundaries and fluidly move across platforms and job types to mitigate labor arbitrage and labor seasonality; and (c) bridging geographically dispersed workers, which allow them to form a supportive space where opportunities for labor are exchanged and debated. We argue that these affective strategies attend to Filipino workers’ labor aspirations through a community-oriented strategy encapsulated in a distinct Filipino cultural value bayanihan, which then shapes the collective “anchoring” of platform workers to navigate a precarious market. We explore the transactional nature underlying this “producer-audience” relationship, the activation of trust and influence through personalized practices and mediated encounters, and the power dynamic underlying these engagements. The paper shows that these strategies also set norms and standards in this largely unregulated sector, playing a role in how labor mobility or precarity are organized locally amid “planetary labor markets”

POLICY PROCESSES SUPPORT THROUGH INTEROPERABILITY WITH SOCIAL MEDIA

Governments of many countries attempt to increase public participation by exploiting the capabilities and high penetration of the Internet. In this direction they make considerable investments for constructing and operating e-participation websites; however, the use of them has been in general limited and below expectations. For this reason governments, in order to widen e-participation, should investigate the exploitation of the numerous users-driven Web 2.0 social media as well, which seem to be quite successful in attracting huge numbers of users. This paper describes a methodology for the exploitation of the Web 2.0 social media by government organizations in the processes of public policies formulation, through a central platform-toolset providing interoperability with many different social media, and enabling posting and retrieving content from them in a systematic centrally managed and machinesupported automated manner (through their application programming interfaces (APIs)). The proposed methodology includes the use of ‘Policy Gadgets’ (Padgets), which are defined as micro web applications presenting policy messages in various popular Web 2.0 social media (e.g. social networks, blogs, forums, news sites, etc) and collecting users’ interactions with them (e.g. views, comments, ratings, votes, etc.). Interaction data can be used as input in policy simulation models estimating the impact of various policy options. Encouraging have been the conclusions from the analysis of the APIs of 10 highly popular social media, which provide extensive capabilities for publishing content on them (e.g. data, images, video, links, etc.) and also for retrieving relevant user activity and content (e.g. views, comments, ratings, votes, etc.), though their continuous evolution might pose significant difficulties and challenges

Combating User Misbehavior on Social Media

Social media encourages user participation and facilitates user’s self-expression like never before. While enriching user behavior in a spectrum of means, many social media platforms have become breeding grounds for user misbehavior. In this dissertation we focus on understanding and combating three specific threads of user misbehaviors that widely exist on social media — spamming, manipulation, and distortion. First, we address the challenge of detecting spam links. Rather than rely on traditional blacklist-based or content-based methods, we examine the behavioral factors of both who is posting the link and who is clicking on the link. The core intuition is that these behavioral signals may be more difficult to manipulate than traditional signals. We find that this purely behavioral approach can achieve good performance for robust behavior-based spam link detection. Next, we deal with uncovering manipulated behavior of link sharing. We propose a four-phase approach to model, identify, characterize, and classify organic and organized groups who engage in link sharing. The key motivating insight is that group-level behavioral signals can distinguish manipulated user groups. We find that levels of organized behavior vary by link type and that the proposed approach achieves good performance measured by commonly-used metrics. Finally, we investigate a particular distortion behavior: making bullshit (BS) statements on social media. We explore the factors impacting the perception of BS and what leads users to ultimately perceive and call a post BS. We begin by preparing a crowdsourced collection of real social media posts that have been called BS. We then build a classification model that can determine what posts are more likely to be called BS. Our experiments suggest our classifier has the potential of leveraging linguistic cues for detecting social media posts that are likely to be called BS. We complement these three studies with a cross-cutting investigation of learning user topical profiles, which can shed light into what subjects each user is associated with, which can benefit the understanding of the connection between user and misbehavior. Concretely, we propose a unified model for learning user topical profiles that simultaneously considers multiple footprints and we show how these footprints can be embedded in a generalized optimization framework. Through extensive experiments on millions of real social media posts, we find our proposed models can effectively combat user misbehavior on social media

Updating democracy studies: outline of a research program

Technologies carry politics since they embed values. It is therefore surprising that mainstream political and legal theory have taken the issue so lightly. Compared to what has been going on over the past few decades in the other branches of practical thought, namely ethics, economics and the law, political theory lags behind. Yet the current emphasis on Internet politics that polarizes the apologists holding the web to overcome the one-to-many architecture of opinion-building in traditional representative democracy, and the critics that warn cyber-optimism entails authoritarian technocracy has acted as a wake up call. This paper sets the problem – “What is it about ICTs, as opposed to previous technical devices, that impact on politics and determine uncertainty about democratic matters?” – into the broad context of practical philosophy, by offering a conceptual map of clusters of micro-problems and concrete examples relating to “e-democracy”. The point is to highlight when and why the hyphen of e-democracy has a conjunctive or a disjunctive function, in respect to stocktaking from past experiences and settled democratic theories. My claim is that there is considerable scope to analyse how and why online politics fails or succeeds. The field needs both further empirical and theoretical work