Predicting User's Political Party using Ideological Stances

Predicting users political party in social media has important impacts on many real world applications such as targeted advertising, recommendation and personalization. Several political research studies on it indicate that political parties' ideological beliefs on sociopolitical issues may influence the users political leaning. In our work, we exploit users' ideological stances on controversial issues to predict political party of online users. We propose a collaborative filtering approach to solve the data sparsity problem of users stances on ideological topics and apply clustering method to group the users with the same party. We evaluated several state-of-the-art methods for party prediction task on debate.org dataset. The experiments show that using ideological stances with Probabilistic Matrix Factorization (PMF) technique achieves a high accuracy of 88.9% at 22.9% data sparsity rate and 80.5% at 70% data sparsity rate on users' party prediction task. ? 2013 Springer International Publishing.EI

The American Flag and the Presidential Election of 1988: Reconsidering the Importance of Valence Issues

In the 1988 Presidential election George W. Bush used the issue of the American flag as a way to demonstrate his own patriotism while undermining Dukakis'. This strategy relied on valence issues, which are issues that are generally considered to be desirable by most voters. Some authors contend that these valence issues are important because they give a candidate an additional advantage that is separate from his/her other issue stances. In this thesis, I contend that valence issues are not considered in isolation, but are instead integral to how voters determine a candidate's ability to handle current and future crises. Using the 1988 National Election Study (NES) a series of regression equations where estimated which found support for this interpretation, adding to our understanding of the importance valence issues and the American flag. Additional studies are needed in order to elucidate these results, but this thesis serves as a guide for future research

Stance detection on social media: State of the art and trends

Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents an exhaustive review of stance detection techniques on social media, including the task definition, different types of targets in stance detection, features set used, and various machine learning approaches applied. The survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, this study explores the emerging trends and different applications of stance detection on social media. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this paper. Please withdraw this article before we finish the new versio

Ecological consciousness and behaviour examined : An empirical study in the Netherlands

Contains fulltext : 3305.pdf (publisher's version ) (Open Access

Argumentation Stance Polarity and Intensity Prediction and its Application for Argumentation Polarization Modeling and Diverse Social Connection Recommendation

Cyber argumentation platforms implement theoretical argumentation structures that promote higher quality argumentation and allow for informative analysis of the discussions. Dr. Liu’s research group has designed and implemented a unique platform called the Intelligent Cyber Argumentation System (ICAS). ICAS structures its discussions into a weighted cyber argumentation graph, which describes the relationships between the different users, their posts in a discussion, the discussion topic, and the various subtopics in a discussion. This platform is unique as it encodes online discussions into weighted cyber argumentation graphs based on the user’s stances toward one another’s arguments and ideas. The resulting weighted cyber argumentation graphs can then be used by various analytical models to measure aspects of the discussion. In prior work, many aspects of cyber argumentation have been modeled and analyzed using these stance relationships. However, many challenging problems remain in cyber argumentation. In this dissertation, I address three of these problems: 1) modeling and measure argumentation polarization in cyber argumentation discussions, 2) encouraging diverse social networks and preventing echo chambers by injecting ideological diversity into social connection recommendations, and 3) developing a predictive model to predict the stance polarity and intensity relationships between posts in online discussions, allowing discussions from outside of the ICAS platform to be encoded as weighted cyber argumentation graphs and be analyzed by the cyber argumentation models. In this dissertation, I present models to measure polarization in online argumentation discussions, prevent polarizing echo-chambers and diversifying users’ social networks ideologically, and allow online discussions from outside of the ICAS environment to be analyzed using the previous models from this dissertation and the prior work from various researchers on the ICAS system. This work serves to progress the field of cyber argumentation by introducing a new analytical model for measuring argumentation polarization and developing a novel method of encouraging ideological diversity into social connection recommendations. The argumentation polarization model is the first of its kind to look specifically at the polarization among the users contained within a single discussion in cyber argumentation. Likewise, the diversity enhanced social connection recommendation re-ranking method is also the first of its kind to introduce ideological diversity into social connections. The former model will allow stakeholders and moderators to monitor and respond to argumentation polarization detected in online discussions in cyber argumentation. The latter method will help prevent network-level social polarization by encouraging social connections among users who differ in terms of ideological beliefs. This work also serves as an initial step to expanding cyber argumentation research into the broader online deliberation field. The stance polarity and intensity prediction model presented in this dissertation is the first step in allowing discussions from various online platforms to be encoded into weighted cyber argumentation graphs by predicting the stance weights between users’ posts. These resulting predicted weighted cyber augmentation graphs could then be used to apply cyber argumentation models and methods to these online discussions from popular online discussion platforms, such as Twitter and Reddit, opening many new possibilities for cyber argumentation research in the future

GPT-4V(ision) as A Social Media Analysis Engine

Recent research has offered insights into the extraordinary capabilities of Large Multimodal Models (LMMs) in various general vision and language tasks. There is growing interest in how LMMs perform in more specialized domains. Social media content, inherently multimodal, blends text, images, videos, and sometimes audio. Understanding social multimedia content remains a challenging problem for contemporary machine learning frameworks. In this paper, we explore GPT-4V(ision)'s capabilities for social multimedia analysis. We select five representative tasks, including sentiment analysis, hate speech detection, fake news identification, demographic inference, and political ideology detection, to evaluate GPT-4V. Our investigation begins with a preliminary quantitative analysis for each task using existing benchmark datasets, followed by a careful review of the results and a selection of qualitative samples that illustrate GPT-4V's potential in understanding multimodal social media content. GPT-4V demonstrates remarkable efficacy in these tasks, showcasing strengths such as joint understanding of image-text pairs, contextual and cultural awareness, and extensive commonsense knowledge. Despite the overall impressive capacity of GPT-4V in the social media domain, there remain notable challenges. GPT-4V struggles with tasks involving multilingual social multimedia comprehension and has difficulties in generalizing to the latest trends in social media. Additionally, it exhibits a tendency to generate erroneous information in the context of evolving celebrity and politician knowledge, reflecting the known hallucination problem. The insights gleaned from our findings underscore a promising future for LMMs in enhancing our comprehension of social media content and its users through the analysis of multimodal information

Beyond Muslim identity: Opinion-based groups in the Gezi Park protest

Media depicted Turkish Gezi Park protests as a clash between secularists and Islamists within a majority-Muslim country. Extending a social identity approach to protests, this study aims (a) to distinguish the protest participants in terms of their opinion-based group memberships, (b) to investigate how their religious identification and their group membership were associated with democratic attitudes. Six hundred and fifty highly educated urban young adult participants were surveyed during the protest. Latent class analysis of participants’ political concerns and online and offline actions yielded four distinct opinion-based groups labeled “liberals,” “secularists,” “moderates,” and “conservatives.” Looking at the intersection of the participants’ group identities with their Muslim identification, we observed that the higher conservatives’ and moderates’ religious identification, the less they endorsed democratic attitudes, whereas religious identification made little or no difference in liberals’ and secularists’ democratic attitudes. Our findings of distinct groups among protest participants in a majority-Muslim country challenge an essentialist understanding of religion as a homogeneous social identity.status: publishe

Modeling the Relationship between a Social Responsibility Attitude and Youth Activism

Despite existing literature that demonstrates the relation between an attitude of social responsibility and activism; few studies have examined the underlying factor structure of social responsibility. The current study had two goals. The first goal was to examine the structure of a measure of social responsibility attitude for urban adolescents. The second goal was to examine the associations of social responsibility with civic and political activism. The participants were 221 adolescents from schools and youth serving organizations in metropolitan Atlanta, GA. Confirmatory factor analysis of social responsibility items revealed that a model with a single latent factor explained the data better than a two-factor model with one latent factor representing neighborhood social responsibility and the other representing global social responsibility. There were significant positive relations between social responsibility and civic activism and political activism when controlling for parental activism and peer activism. This study suggests that a social responsibility attitude may exist as a single factor amongst urban adolescents and it has added empirical support to show that higher levels of social responsibility are associated with greater depth of involvement in civic and political activism. Implications for both theory and practice are discussed

Understanding misinformation on Twitter in the context of controversial issues

Social media is slowly supplementing, or even replacing, traditional media outlets such as television, newspapers, and radio. However, social media presents some drawbacks when it comes to circulating information. These drawbacks include spreading false information, rumors, and fake news. At least three main factors create these drawbacks: The filter bubble effect, misinformation, and information overload. These factors make gathering accurate and credible information online very challenging, which in turn may affect public trust in online information. These issues are even more challenging when the issue under discussion is a controversial topic. In this thesis, four main controversial topics are studied, each of which comes from a different domain. This variation of domains can give a broad view of how misinformation is manifested in social media, and how it is manifested differently in different domains. This thesis aims to understand misinformation in the context of controversial issue discussions. This can be done through understanding how misinformation is manifested in social media as well as by understanding people’s opinions towards these controversial issues. In this thesis, three different aspects of a tweet are studied. These aspects are 1) the user sharing the information, 2) the information source shared, and 3) whether specific linguistic cues can help in assessing the credibility of information on social media. Finally, the web application tool TweetChecker is used to allow online users to have a more in-depth understanding of the discussions about five different controversial health issues. The results and recommendations of this study can be used to build solutions for the problem of trustworthiness of user-generated content on different social media platforms, especially for controversial issues