Search Bias Quantification: Investigating Political Bias in Social Media and Web Search

Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources—input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems—Twitter social media search and Google web search—for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.publishe

Systematically Monitoring Social Media: the case of the German federal election 2017

It is a considerable task to collect digital trace data at a large scale and at the same time adhere to established academic standards. In the context of political communication, important challenges are (1) defining the social media accounts and posts relevant to the campaign (content validity), (2) operationalizing the venues where relevant social media activity takes place (construct validity), (3) capturing all of the relevant social media activity (reliability), and (4) sharing as much data as possible for reuse and replication (objectivity). This project by GESIS - Leibniz Institute for the Social Sciences and the E-Democracy Program of the University of Koblenz-Landau conducted such an effort. We concentrated on the two social media networks of most political relevance, Facebook and Twitter.Comment: PID: http://nbn-resolving.de/urn:nbn:de:0168-ssoar-56149-4, GESIS Papers 2018|

Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings

In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. We show these representations to be useful not only for categorizing users, but also for automatically generating user and community profiles. Inspired by traditional summarization approaches, we create the profiles by selecting diverse and representative content from all available modalities, i.e. the text, image and user modality. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations, to compare different embedding strategies, and to determine the importance of different modalities. We demonstrate the capabilities of the proposed approach on two different multimedia collections originating from the violent online extremism forum Stormfront and the microblogging platform Twitter, which are particularly interesting due to the high semantic level of the discussions they feature

Seminar Users in the Arabic Twitter Sphere

We introduce the notion of "seminar users", who are social media users engaged in propaganda in support of a political entity. We develop a framework that can identify such users with 84.4% precision and 76.1% recall. While our dataset is from the Arab region, omitting language-specific features has only a minor impact on classification performance, and thus, our approach could work for detecting seminar users in other parts of the world and in other languages. We further explored a controversial political topic to observe the prevalence and potential potency of such users. In our case study, we found that 25% of the users engaged in the topic are in fact seminar users and their tweets make nearly a third of the on-topic tweets. Moreover, they are often successful in affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201

Tracking the History and Evolution of Entities: Entity-centric Temporal Analysis of Large Social Media Archives

How did the popularity of the Greek Prime Minister evolve in 2015? How did the predominant sentiment about him vary during that period? Were there any controversial sub-periods? What other entities were related to him during these periods? To answer these questions, one needs to analyze archived documents and data about the query entities, such as old news articles or social media archives. In particular, user-generated content posted in social networks, like Twitter and Facebook, can be seen as a comprehensive documentation of our society, and thus meaningful analysis methods over such archived data are of immense value for sociologists, historians and other interested parties who want to study the history and evolution of entities and events. To this end, in this paper we propose an entity-centric approach to analyze social media archives and we define measures that allow studying how entities were reflected in social media in different time periods and under different aspects, like popularity, attitude, controversiality, and connectedness with other entities. A case study using a large Twitter archive of four years illustrates the insights that can be gained by such an entity-centric and multi-aspect analysis.Comment: This is a preprint of an article accepted for publication in the International Journal on Digital Libraries (2018

Linking for influence: Twitter linked content in the Scottish Referendum televised debates.

Twitter, the micro-blogging social media tool, has established a critical role in facilitating social engagement. Its low technical and economic barriers to uptake provide a readily accessible forum for public engagement with events such as televised political debates, and in this context provides a 'backchannel' to mainstream media, allowing users to comment on and engage in debates. Most recently during the 2014 Scottish Referendum, Twitter was used extensively by both 'Better Together' (pro-Unionist) and 'Yes' (pro-independence) campaigners. The aim of this research was to develop an understanding of the linked content present in tweets sent during three televised debates on the issue of Scottish Independence. Analysis of the linked content shows a broad subject proximity to the topics under discussion during the debates, but highlights the lack of specificity in relation to the peaks and troughs of Twitter traffic during the debates. The paper also highlights the use made of links to a variety of resources such as the mainstream media as well as more informal sources including user-generated image and video content to support political viewpoints, and argues that, while the use of such content is beneficial in terms of unifying perspectives, supporter activism and the gratification of the social need for connectivity, it does not act to convert political opinion

Exploring the democratic potential of online social networking: The scope and limitations of e-participation

Copyright © 2012 by the Association for Information Systems.The availability and promise of social networking technologies with their perceived open philosophy has increasingly inspired citizens around the world to participate in political activity on the Web. Recent examples range from opposing public policies, such as government funding cuts, to organizing revolutionary social movements, such as those in the Middle East and North Africa. Although online spaces create remarkable opportunities for various forms of political action, there are concerns over the power of existing institutions to control and even censor such interaction spaces. The objective of this article is to draw together different insights on the online engagement phenomenon, highlighting both its potential and limitations as a mechanism for fostering democratic debate and influencing policy making. We examine recent examples from Europe, the Middle East and Latin America. Finally, we summarize the implications of our work and outline directions for further research

Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams

Online social media are complementing and in some cases replacing person-to-person social interaction and redefining the diffusion of information. In particular, microblogs have become crucial grounds on which public relations, marketing, and political battles are fought. We introduce an extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events. We describe a Web service that leverages this framework to track political memes in Twitter and help detect astroturfing, smear campaigns, and other misinformation in the context of U.S. political elections. We present some cases of abusive behaviors uncovered by our service. Finally, we discuss promising preliminary results on the detection of suspicious memes via supervised learning based on features extracted from the topology of the diffusion networks, sentiment analysis, and crowdsourced annotations

Hashtag activism and the configuration of counterpublics: Dutch animal welfare debates on Twitter

Social media platforms provide major opportunities for online activism and the emergence of digital counterpublics. Research on counterpublics has focused on actors and their narrative strategies aiming at deconstructing dominant discourses. Less attention has been paid to how the interplay between actors and platform-specific functions affects the configurations and therewith also the success of digital counterpublics. Existing studies mainly rely on determining up front which topics, actor characteristics, or arguments constitute hashtag activism and digital counterpublics. In contrast, our approach allows for an empirical identification based on how actors position themselves in an online debate toward other actors and their shared hashtags. We argue that online activism is co-constituted by actors and their usage of hashtags, actor mentions, and retweets. Applying a communicative network perspective allows for the integration of semantic and relational research traditions. We combine a recently developed automated network analysis method and content analysis to analyze two Twitter debates about animal welfare issues. Our results show that among Twitter users, citizens and environmental organizations formed a common cluster whereas media actors formed their own sub-clusters in both debates. The findings emphasize the central role of citizens for the configuration of digital counterpublics. The proposed approach can be further adapted and applied more widely for the analysis of online activism and debates