Shared contexts, shared background, shared values – Homophily in the Finnish parliament members’ social networks on Twitter

While Twitter has become an essential part of daily politics across Western countries, little research has focused on origins of politicians’ social circles on social media. This paper contributes to how structural, ideological and contextual factors affect tie formation between parliamentarians’ Twitter networks. The study focuses on Finland, where over 80 percent of parliamentarians are using the platform. For empirical analysis, we first extracted parliamentarians’ followee network connections from their Twitter accounts (36 294 nodes and 113 108 edges) and combined it with data from a national voting advice application, which includes information regarding parliamentarians' societal position and opinions regarding social, cultural and economic issues. According to the explanatory analysis, we found that connections between parliamentarians and the share of mutual followees are clearly based on matching values, similar background and shared contextual factors. Additionally, we found that shared context had strong confounding effects on the function of value homophily in relations and shared networks between Finnish parliamentarians.</p

Carthago Delenda Est: Co-opetitive Indirect Information Diffusion Model for Influence Operations on Online Social Media

For a state or non-state actor whose credibility is bankrupt, relying on bots to conduct non-attributable, non-accountable, and seemingly-grassroots-but-decentralized-in-actuality influence/information operations (info ops) on social media can help circumvent the issue of trust deficit while advancing its interests. Planning and/or defending against decentralized info ops can be aided by computational simulations in lieu of ethically-fraught live experiments on social media. In this study, we introduce Diluvsion, an agent-based model for contested information propagation efforts on Twitter-like social media. The model emphasizes a user's belief in an opinion (stance) being impacted by the perception of potentially illusory popular support from constant incoming floods of indirect information, floods that can be cooperatively engineered in an uncoordinated manner by bots as they compete to spread their stances. Our model, which has been validated against real-world data, is an advancement over previous models because we account for engagement metrics in influencing stance adoption, non-social tie spreading of information, neutrality as a stance that can be spread, and themes that are analogous to media's framing effect and are symbiotic with respect to stance propagation. The strengths of the Diluvsion model are demonstrated in simulations of orthodox info ops, e.g., maximizing adoption of one stance; creating echo chambers; inducing polarization; and unorthodox info ops, e.g., simultaneous support of multiple stances as a Trojan horse tactic for the dissemination of a theme.Comment: 60 pages, 9 figures, 1 tabl

#BlackLivesMatter: critical political implications of Twitter discourse in the wake of George Floyd

As we move forward into a digitalized age of communication, social media becomes increasingly powerful with each passing day. Digital forms of interaction foster an active political discourse and influence the behavior of both the public and government officials. The freedom to deliver public statements at will has been effectively utilized by individuals, local activists, and representatives of the legislative branch to interact in discourse concerning current events, therefore furthering the mobilization of social media to shape the American public policy stage as a whole. As a result of the accessibility of the internet, social media has become the most effective way for individual citizens to communicate directly with their representatives. Due to their prominence and accessibility, these virtual platforms have become integral to communication and political engagement in the United States, specifically among those concerned with social change. My research answers the question of how the Black Lives Matter movement has been categorized into major themes through online discourse with regard to who begins narratives, how they spread, and what they become through the use of Twitter. A critical discourse analysis of such findings reveals the nature of online political discourse, how the use of social media impacts the setting of the American political agenda, and what the future of further digitalized political engagement may look like

Information Propagation on Social Networks

Many models of disease and rumor spreading phenomena average the behavior of individuals in a population in order to obtain a coarse description of expected system behavior. For these types of models, we determine how close the coarse population-level approximation is to its corresponding agent-based system and discuss the accuracy of the population-level approximation. We apply these theoretical results to real social network data to see how well they describe the contagious nature of social phenomena. Specifically, we consider hashtag adoption data collected from the Twitter social network. To assimilate the Twitter data to a simple contagion model, we developed and implemented statistical learning methods to construct an adaptive state estimator for systems described by nonlinear stochastic differential equations. We found that the static network structure alone is not sufficient for explaining hashtag adoption among users in the Twitter social network, and our result suggest that a user-centric model would be more appropriate for this task. We propose a model for individual social media users, termed a \textit{genotype}, which is a \textit{per-topic} summary of a user's interest, activity and susceptibility to adopt new information. We show that the genotype framework is capable of accurately quantifying the adoption behavior of individual users with respect to hashtag topics

Technologically Mediated Discourse and Information Exchange through Medium Specific Syntactical Features: The 2012 Presidential Election on Twitter

Political discourse has been historically constrained by geographic proximity of participants. The introduction of the Internet and specifically social media has altered these geographic constraints and political discourse is now one of the most prevalent activities in social media. The increasing use of technology to acquire political information and participate in the political process in the United States creates a gap between what is understood about political activity in a democratic society and the specific technological features people use. As more individuals begin to use technology for political activity, understanding how the technology is used becomes increasingly important. Previous research exploring political discourse on social media has focused on one discrete event or a narrow time period. This narrow focus limits the understanding of the complex environment that comprises an election. This study takes a longitudinal approach and uses network analysis, co-occurrence analysis and temporal frequency analysis to examine a 53 million Twitter message (tweet) corpus collected during the 2012 Presidential Election (August 20, 2012 - November 13, 2012) to understand how individuals use Twitter to engage in political discourse. The queries used to compose the dataset were theoretically informed based on democratic theory and previous socio-technical research. This study makes three contributions to the existing literature. First, this study identifies that individuals use syntactical features differently in the context of an acute event such as a debate. Second, this study indicates that, although candidates and media are the most talked about and talked to, these interactions elicit no response. Third, this study reveals that information shared through URLs was predominantly user-generated content from Twitter and mass media information suggesting a reflexive information-sharing environment. This study illustrates that even with the availability of the numerous technological and syntactical features to facilitate interactions and share information, there is still a limited realization of the promise that technologies such as Twitter afford. Instead of fundamentally changing the political discourse process by having individuals use it for two-way communication, Twitter amplifies the existing political environment where there is limited cohesive discourse and communication is one-way.Ph.D., Information Studies -- Drexel University, 201

Internet Mediated NGO Activity: How Environmental NGOs use Weibo in China

This thesis uses an interdisciplinary approach that draws on both the political science and media and communication fields to analyse how Chinese environmental NGOs use the microblogging site, Sina Weibo, in their online activism. The study of NGOs and how they use the internet in China is widespread. However, in many cases, the way that NGOs in China work, both online and offline, has been analysed through the lens of traditional civil society and internet studies literature, which has mostly focused on the ability of NGOs, and the internet, to give rise to significant political change, and even democratisation.Through a mixture of thematic, network, and organisational analysis, this thesis investigates the communicative functions, themes, and use of interactive features in posts on Weibo, including the use of hashtags, retweets and @mentions. At the organisational level, the ways that NGOs engage with different actors, both online and offline, including fellow NGOs, government departments, their followers, and potential donors are interrogated using four case studies. These analyses found that although the political space afforded to environmental NGOs in China is severely constrained, and the operations of the NGOs could not be seen as overtly activist or confrontational in the traditional sense, the NGOs do in fact retain a certain amount of autonomy and are able to carve out some political space for themselves. The findings of this thesis therefore challenge the notion that NGOs in China are co-opted organisations without autonomy from the state and suggests that there is scope for digital activism by NGOs in an authoritarian context, even though the online and offline political space they inhabit may be tightly regulated and controlled

Analysing political events on Twitter: topic modelling and user community classification

Recently, political events, such as elections or referenda, have raised a lot of discussions on social media networks, in particular, Twitter. This brings new opportunities for social scientists to address social science tasks, such as understanding what communities said, identify- ing whether a community has an influence on another or analysing how these communities respond to political events online. However, identifying these communities and extracting what they said from social media data are challenging and non-trivial tasks. In this thesis, we aim to make progress towards understanding ‘who’ (i.e. communities) said ‘what’ (i.e. discussed topics) and ‘when’ (i.e. time) during political events on Twitter. While identifying the ‘who’ can benefit from Twitter user community classification approaches, ‘what’ they said and ‘when’ can be effectively addressed on Twitter by extracting their discussed topics using topic modelling approaches that also account for the importance of time on Twitter. To evaluate the quality of these topics, it is necessary to investigate how coherent these topics are to humans. Accordingly, we propose a series of approaches in this thesis. First, we investigate how to effectively evaluate the coherence of the topics generated using a topic modelling approach. The topic coherence metric evaluates the topical coherence by examining the semantic similarity among words in a topic. We argue that the semantic similarity of words in tweets can be effectively captured by using word embeddings trained using a Twitter background dataset. Through a user study, we demonstrate that our proposed word embedding-based topic coherence metric can assess the coherence of topics like humans. In addition, inspired by the precision at k information retrieval metric, we propose to evaluate the coherence of a topic model (containing many topics) by averaging the top-ranked topics within the topic model. Our proposed metrics can not only evaluate the coherence of topics and topic models, but also can help users to choose the most coherent topics. Second, we aim to extract topics with a high coherence from Twitter data. Such topics can be easily interpreted by humans and they can assist to examine ‘what’ has been discussed on Twitter and ‘when’. Indeed, we argue that topics can be discussed in different time periods and therefore can be effectively identified and distinguished by considering their time periods. Hence, we propose an effective time-sensitive topic modelling approach by integrating the time dimension of tweets (i.e. ‘when’). We show that the time dimension helps to generate topics with a high coherence. Hence, we argue that ‘what’ has been discussed and ‘when’ can be effectively addressed by our proposed time-sensitive topic modelling approach. Next, to identify ‘who’ participated in the topic discussions, we propose approaches to identify the community affiliations of Twitter users, including automatic ground-truth generation approaches and a user community classification approach. To generate ground-truth data for training a user community classifier, we show that the mentioned hashtags and entities in the users’ tweets can indicate which community a Twitter user belongs to. Hence, we argue that they can be used to generate the ground-truth data for classifying users into communities. On the other hand, we argue that different communities favour different topic discussions and their community affiliations can be identified by leveraging the discussed topics. Accordingly, we propose a Topic-Based Naive Bayes (TBNB) classification approach to classify Twitter users based on their words and discussed topics. We demonstrate that our TBNB classifier together with the ground-truth generation approaches can effectively identify the community affiliations of Twitter users. Finally, to show the generalisation of our approaches, we apply our approaches to analyse 3.6 million tweets related to US Election 2016 on Twitter. We show that our TBNB approach can effectively identify the ‘who’, i.e. classify Twitter users into communities by using hashtags and the discussed topics. To investigate ‘what’ these communities have discussed, we apply our time-sensitive topic modelling approach to extract coherent topics. We finally analyse the community-related topics evaluated and selected using our proposed topic coherence metrics. Overall, we contribute to provide effective approaches to assist social scientists towards analysing political events on Twitter. These approaches include topic coherence metrics, a time-sensitive topic modelling approach and approaches for classifying the community affiliations of Twitter users. Together they make progress to study and understand the connections and dynamics among communities on Twitter

Information credibility perception on Twitter

Information on Twitter is vast and varied. Readers must make their own judgements to determine the credibility of the great wealth of information presented on Twitter. This research aims to identify the factors that influence readers&#039; judgements of the credibility of information on Twitter, especially news-related information. Both internal (within the Twitter platform) and external factors are studied in this research. User studies are conducted to collect readers&#039; perceptions of the credibility of news-related tweets, Twitter features, and the impact of reader characteristics, such as a reader&#039;s demographic attributes, their personality and behaviour. Twitter readers are found to depend solely on surface tweet features in making these judgements such as the author&#039;s Twitter ID, pictures, or the number of retweets and likes, rather than the tweet&#039;s metadata as recommended in previous studies. In this study, surface features are related to cognitive heuristics. Cognitive heuristics are features that the mind uses as shortcuts for making quick evaluations such as deciding the credibility of tweets. There are three main types of cognitive heuristic features found on Twitter that readers use to determine credibility: endorsement, reputation and confirmation. This study finds that readers do not use only one single feature to make credibility judgements but rather a combination of features. External factors such as a reader&#039;s educational background and geolocation also have a significant positive correlation with their perceptions of a tweet&#039;s credibility. Readers with tertiary level education, or living in a certain location or environment, such as in a crisis or conflict area, are observed to be more careful in making credibility judgements. Readers who possess conscientiousness and openness to experience personality traits are also seen to be very cautious in their credibility judgements. Another insight provided by this research is the categorisation of readers&#039; behaviours according to credibility perceptions on Twitter. The behavioural categorisations are defined by readers&#039; behavioural reliance on Twitter&#039;s surface features when judging the credibility of tweets. The findings can assist social media authors in designing the surface features of their social media content in order to enhance the content&#039;s credibility. Furthermore, findings from this research can help in developing effective credibility evaluation systems by considering readers&#039; personal characteristics

Congress, Constituents, and Social Media: Understanding Member Communications in the Age of Instantaneous Communication

Building the first database on Congressional use of traditional and social media, this project examined media use by Members in the 115th Congress. The team collected data on the use of both traditional media (franking disbursements, communication staff) and new media (social media, e-newsletters). Analysis indicates that (1) Member ideology has an effect on use of franking and on size of communication staff and (2) the more members spoke on the floor, the more Facebook posts they made