clicktatorship and democrazy: Social media and political campaigning

This chapter aims to direct attention to the political dimension of the social media age. Although current events like the Cambridge Analytica data breach managed to raise awareness for the issue, the systematically organized and orchestrated mechanisms at play still remain oblivious to most. Next to dangerous monopoly-tendencies among the powerful players on the market, reliance on automated algorithms in dealing with content seems to enable large-scale manipulation that is applied for economical and political purposes alike. The successful replacement of traditional parties by movements based on personality cults around marketable young faces like Emmanuel Macron or Austria’s Sebastian Kurz is strongly linked to products and services offered by an industry that simply provides likes and followers for cash. Inspired by Trump’s monopolization of the Twitter-channel, these new political acteurs use the potential of social media for effective message control, allowing them to avoid confrontations with professional journalists. In addition, an extremely active minority of organized agitators relies on the viral potential of the web to strongly influence and dictate public discourse – suggesting a shift from the Spiral of Silence to the dangerous illusion of a Nexus of Noise

Vortex of the Web. Potentials of the online environment

This volume compiles international contributions that explore the potential risks and chances coming along with the wide-scale migration of society into digital space. Suggesting a shift of paradigm from Spiral of Silence to Nexus of Noise, the opening chapter provides an overview on systematic approaches and mechanisms of manipulation – ranging from populist political players to Cambridge Analytica. After a discussion of the the juxtaposition effects of social media use on social environments, the efficient instrumentalization of Twitter by Turkish politicans in the course of the US-decision to recognize Jerusalem as Israel’s capital is being analyzed. Following a case study of Instagram, Black Lives Matter and racism is a research about the impact of online pornography on the academic performance of university students. Another chapter is pointing out the potential of online tools for the successful relaunch of shadow brands. The closing section of the book deals with the role of social media on the opinion formation about the Euromaidan movement during the Ukrainian revolution and offers a comparative study touching on Russian and Western depictions of political documentaries in the 2000s

Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback

Recommender systems widely use implicit feedback such as click data because of its general availability. Although the presence of clicks signals the users' preference to some extent, the lack of such clicks does not necessarily indicate a negative response from the users, as it is possible that the users were not exposed to the items (positive-unlabeled problem). This leads to a difficulty in predicting the users' preferences from implicit feedback. Previous studies addressed the positive-unlabeled problem by uniformly upweighting the loss for the positive feedback data or estimating the confidence of each data having relevance information via the EM-algorithm. However, these methods failed to address the missing-not-at-random problem in which popular or frequently recommended items are more likely to be clicked than other items even if a user does not have a considerable interest in them. To overcome these limitations, we first define an ideal loss function to be optimized to realize recommendations that maximize the relevance and propose an unbiased estimator for the ideal loss. Subsequently, we analyze the variance of the proposed unbiased estimator and further propose a clipped estimator that includes the unbiased estimator as a special case. We demonstrate that the clipped estimator is expected to improve the performance of the recommender system, by considering the bias-variance trade-off. We conduct semi-synthetic and real-world experiments and demonstrate that the proposed method largely outperforms the baselines. In particular, the proposed method works better for rare items that are less frequently observed in the training data. The findings indicate that the proposed method can better achieve the objective of recommending items with the highest relevance.Comment: accepted at WSDM'2

Do reviews from friends and the crowd affect online consumer posting behaviour differently?

User-generated reviews are valuable resources for consumers to gain information of products which has significant impact on their following decision-making. With the development of social network service, consumers are exposed to reviews coming from both friends and the crowds (non-friends). However, the impact of friends’ and crowds’ reviews on consumer posting behaviour has not been well differentiated. Using the online review information as well as the underlying social network from Yelp, this paper develops a multilevel mixed effect probit model to study the impact of consumer characteristics and reviews of different sources, i.e. friends or crowds, on the possibility of consumer further engaging in posting behaviour. Despite the common perception that the volume, valance and variance of reviews significantly impact the possibility of following posting behaviour, we show that such influence majorly comes from the friend reviews. The volume of friend reviews has much stronger impact on the target user’s posting behaviour than that of the crowds. The valance and variance of the crowd reviews show no significant influence when ignoring the friend reviews, but negative influence when considering it. The friend reviews and crowd reviews are further divided as positive and negative ones, and only the positive friend reviews and negative crowd review are found significantly enhancing the posting possibility

Dual Contrastive Network for Sequential Recommendation with User and Item-Centric Perspectives

With the outbreak of today's streaming data, sequential recommendation is a promising solution to achieve time-aware personalized modeling. It aims to infer the next interacted item of given user based on history item sequence. Some recent works tend to improve the sequential recommendation via randomly masking on the history item so as to generate self-supervised signals. But such approach will indeed result in sparser item sequence and unreliable signals. Besides, the existing sequential recommendation is only user-centric, i.e., based on the historical items by chronological order to predict the probability of candidate items, which ignores whether the items from a provider can be successfully recommended. The such user-centric recommendation will make it impossible for the provider to expose their new items and result in popular bias. In this paper, we propose a novel Dual Contrastive Network (DCN) to generate ground-truth self-supervised signals for sequential recommendation by auxiliary user-sequence from item-centric perspective. Specifically, we propose dual representation contrastive learning to refine the representation learning by minimizing the euclidean distance between the representations of given user/item and history items/users of them. Before the second contrastive learning module, we perform next user prediction to to capture the trends of items preferred by certain types of users and provide personalized exploration opportunities for item providers. Finally, we further propose dual interest contrastive learning to self-supervise the dynamic interest from next item/user prediction and static interest of matching probability. Experiments on four benchmark datasets verify the effectiveness of our proposed method. Further ablation study also illustrates the boosting effect of the proposed components upon different sequential models.Comment: 23 page

When One Speaks Out and When One Does Not: Online Discussion Forums for Opinion Expression

Individuals’ opinion expression about public affairs has entered a new phase with the growth of new venues for social interaction among fellow citizens such as online discussion forums. However, not much empirical evidence exists to understand an individual’s voicing views in online discussion. Focusing on this attention-deserved form of political activity online, the current dissertation aimed to yield insights into some fundamental questions: who, with what characteristics, more intends and tends to talk on an online discussion forum, and what forum conditions (and combinations of them) facilitate an individual’s opinion expression intention and behavior. To investigate these questions, two experimental research methods – scenario-based thought and website-based true experiments – were implemented. Thought experiments relied on a hypothetical scenario technique, the most widely used method in spiral of silence research, but employed the multifaceted, detailed scenarios. True experiments, on the other hand, used the stimulus online forums designed for this study to actually place the participants in the online discussion situation. The findings from these two different approaches indicated that a person’s race, issue involvement, issue knowledge, and the revelation of identity were factors that generally influenced opinion expression online. Racial minorities, compared to Whites, were consistently more willing and likely to voice their views on the online forum. Those who were involved in and knowledgeable about the issue under discussion were more likely to post messages to the forum. Disclosing one’s real name and other personal information was a big hindrance to actual opinion expression on the discussion forum. However, comparing the findings from scenarios to those obtained from real, analogous situations also revealed that the use of scenarios could not accurately identify some existing phenomena. Thought and true experiments returned incongruent predictions regarding the roles of age, fear of isolation, and the votes climate as well as the contribution degree of issue knowledge (to posting intention). In particular, trait fear of isolation, which has been pointed out as the primary culprit behind silencing minority opinion holders, played a completely opposite role. Against the background of these findings, the theoretical and methodological implications of the study were discussed.PhDCommunication StudiesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116699/1/ywoh_1.pd