Search

Mounting uncertainties regarding the coronavirus disease (COVID-19) pandemic and the popularity of social media created fertile grounds for conspiracy theories to flourish, leading to a global “infodemic.” We examine information sources used to support five popular COVID-19-related conspiracy theories on Twitter to identify (1) their primary building blocks, (2) similarities and dissimilarities across COVID-19 conspiracy theories, and (3) the relationship between type of message content and content distribution. Findings show that statements of belief and of malicious purpose were most popular, followed by conspirators, authentication, and secretive actions. However, only malicious purposes and secretive actions messages successfully predicted higher distribution of content, while, for instance, content authentication showed a negative relation. Furthermore, the type of conspiracy theories matters. Mega-theories, such as Agenda 21 and QAnon, incorporated less statements of Belief. COVID-19 vaccine–related theories focused more on authentication, while QAnon highlighted the conspirators behind the pandemic. Conceptual and practical implications are discussed.

Advancements in computing, technology, and their applications to advertising enable marketers to deliver brand messages tailored to individuals and consumer segments. The growth of computational advertising (CA) has created new opportunities but also poses risks in the use of algorithms to generate and optimize the impact of such messages. This article addresses a particular domain influenced by these advancements, namely, automated brand-generated content. We offer an automated brand-generated content (ABC) model that posits two advances. First, rather than solely optimizing consumer data for enhanced impact of automated content, we submit, and provide extra key variables to further illustrate, that there is a desirable balance of both consumer and brand data as inputs to algorithms to serve short- and long-term impact goals. Second, this article guides research by addressing tensions between understanding the relationship between inputs and desired impacts (both short and long term) and proposing a research agenda for future work.

We examined hashtag activism promoting Taiwan’s participation in the global efforts to combat COVID-19. We employed the computational Analysis of Topic Model Networks (ANTMN) to examine the discourse around the #TaiwanCanHelp/#TWforWHO campaign in 2020 ( N = 163,876) on Twitter. Our model identified 35 topics clustered within three frames. The containment frame emphasized strategies used to stop COVID-19’s spread in Taiwan. The geopolitics frame described China’s use of its international power to exclude Taiwan from the World Health Organization. The international cooperation frame emphasized Taiwan’s ability and efficacy to contribute to the global efforts to slow down COVID-19. These results extend our understanding of hashtag activism by examining the intersection of geopolitics and global health crises. We introduce the theoretical concept of a mutually beneficial coalition, one that points to detrimental impacts of oppression on both the oppressed and the allies who are asked to help.

This chapter guides you through the collection, analysis and visualization networks of Facebook public pages. Social media researchers and practitioners often seek to evaluate organizational social media activity and suggest strategies for improvement. Facebook networks can also capture intra-organizational dynamics, for organizations that are large enough to have many public pages. An organization can benefit from a coherent and interconnected structure of its related Facebook pages, allowing Facebook users to easily navigate across a variety of brands, for instance, associated with a company. Furthermore, each Facebook page can become a gateway to different groups of audiences (network clusters), to maximize the exposure of a company’s content on Facebook. Identifying clusters of pages, including their levels of interconnectivity (“Group Edges”) can help an organization to find its best balance between reaching out to a common audience and using its affiliated Facebook pages to bridge out to new audiences.

This chapter guides you from the early stages of data collection of Twitter topical conversations (i.e., search networks), through the analysis of the network structure at the vertex, cluster and network-levels, and key content characteristics of tweets, to visualization of the network. The process is illustrated by analyzing the #p2 (Progressives 2.0) twitter search network. The NodeXL Twitter importers include many Twitter metrics (e.g., Twitter Followers) and content (e.g., Tweet content). Network analysis of Twitter networks, created by individuals, such as activists or consumers, as they discussing brands, organizations or issues, can capture clusters—subgroups of interconnected users—their key information sources and distinctive content characteristics they post and share (i.e., via posted URLs). Furthermore, it allows researchers and brand managers to identify keys users in the network and consumers that allow the brand to reach out to other users that do not interact with it directly.

Making sense of large and complex networks requires filtering and grouping as illustrated with the U.S. Senate co-voting network and CSCW Twitter network. Users can filter based on edge values, vertex metrics, and attribute data (e.g., Twitter followers). Even groups can be filtered out using the Visibility column. The Dynamic Filters feature allows you to interactively determine what is displayed on the graph by setting the position of sliders representing starting and ending ranges. Grouping vertices into subgroups (i.e., groups, clusters, communities, subgraphs) helps reveal important structures such as the Republican and Democratic divide. Subgroups can be created manually (e.g., using attribute data) or automatically (using one of NodeXL’s built in community detection algorithms). They can be visualized using unique vertex color and shape combinations, positioned using the group-in-a-box technique, or collapsed into single units or summary network motifs. Subgraph images can be created for each vertex.

Social movements are increasingly using social media, and Twitter in particular, to reach existing and new publics and advance their mission. While historically movements had to rely on traditional media to connect with such publics, via social media any user can share content, helping to connect the key players within the movement to new publics. The quality of highly shared content, however, has been under scrutiny. The #BlackLivesMatter movement, one of the most prolific and popular hashtag movements on Twitter, is examined as a case study, in order to determine the role of content importance and emotion on the virality of the movement. Four aspects of importance were examined – Policy or Action, Group, Social Actor and Politics– as well as direction of sentiment. Findings suggest important tweets were more likely to be retweeted, where tweets associated with Policy or Action showing the strongest relationship with retweeting. Tweets with expressed emotion were more likely to be retweeted than neutral tweets. When tweets contained important content related to Policy or Action or a Group as well as sentiment, these tweets were most likely to be retweeted, revealing a method for social movements to increase effectiveness. Theoretical and practical implications are discussed.

This study integrates a brand community theoretical framework and a social networks approach to the study of political marketing on social media. It analyzes Twitter activity by and about all Repub...

Individuals increasingly rely on the Internet, and social media in particular, for health-related information. A recent survey reports that 80% of Internet users search for health information online. In the present study, we employ Twitter data to understand content characteristics and the patterns of content flow of the conversations about the HPV vaccine debate. Approaching the HPV vaccine conversations on Twitter as a social network, we can identify key self-formed subgroups-clusters of users who create "siloes" of interactions and information flow. Combining network analysis, computer-aided content analysis, and human-coded content analysis, we explored the communication dynamics within the groups in terms of group members' affective and cognitive characteristics. Our findings show that positive emotion is positively correlated with cluster density, an indicator of strong ties and rapid information flow. In the case of negative emotion, we found that anger is a significant negative predictor for graph density. We also found a correlation between certainty and tentativeness; both at cluster as well as at tweet level, suggesting that clusters bring together people who are sure about the HPV vaccine and people who are exploring for answers.

Advertising researchers and practitioners are increasingly using social media analytics (SMA), but focused overviews that explain how to use various SMA techniques are scarce. We focus on how resea...

This study explored how social media, especially Twitter, serves as a viable place for communicating about cancer. Using a 2-step analytic method that combined social network analysis and computer-aided content analysis, this study investigated (a) how different types of network structures explain retweeting behavior and (b) which types of tweets are retweeted and why some messages generate more interaction among users. The analysis revealed that messages written by users who had a higher number of followers, a higher level of personal influence over the interaction, and closer relationships and similarities with other users were retweeted. In addition, a tweet with a higher level of positive emotion was more likely to be retweeted, whereas a tweet with a higher level of tentative words was less likely to be retweeted. These findings imply that Twitter can be an effective tool for the dissemination of health information. Theoretical and practical implications for psychosocial interventions for people with health concerns are discussed.

Social media platforms provide world governments with the opportunity to distribute news content from their broadcast channels directly to foreign publics [Wallerstein, I. (1974). The modern world system. New York: Academic Press] World System Theory, which has successfully explained and predicted the structure of international news flow, is now being challenged. Specifically, these social platforms undermined assumptions regarding the one-way flow of information toward audiences and the exclusive institutional nature of the players in the international system. This study examines the unique case of government-sponsored news media and its international news flow. It finds that while the structure of the international news flow on Twitter exhibits a hierarchical core–periphery structure, non-institutional actors (e.g. bloggers) conformed less than institutional players (e.g. governments and news media) to that structure. This study also found that non-institutional actors assumed the role of bridgi...

The aim of the present study was to examine the links between severities of child abuse (physical vs. sexual), and alcohol use versus problems via social media (Facebook) peer connection structures.A total of 318 undergraduate female students at a public university in the United States reported severity of child abuse experiences and current alcohol use and problems. Social network data were obtained directly from the individuals' Facebook network.Severity of childhood physical abuse was positively linked to alcohol use and problems via eigenvector centrality, whereas severity of childhood sexual abuse was negatively linked to alcohol use and problems via clustering coefficient.Childhood physical and sexual abuse were linked positively and negatively, respectively, to online social network patterns associated with alcohol use and problems. The study suggests the potential utility of these online network patterns as risk indices and ultimately using social media as a platform for targeted preventive interventions.

Background: This paper goes beyond detecting specific themes within Zika-related chatter on Twitter, to identify the key actors who influence the diffusive process through which some themes are more\ud amplified than others are.\ud \ud Methods: We collected all Zika-related tweets during the three months immediately after the first U.S. case of Zika. Following categorization into twelve themes, a cross-section of tweets were grouped into weekly datasets to capture 12 amplifier/user groups and analysed by four\ud amplification modes: mentions, retweets, talkers, and twitter-wide amplifiers.\ud \ud Results: We analysed 3,057,130 tweets in the US and categorized 4,997 users. The most talked about theme was Zika transmission (~58%). News media, public health institutions and grassroots users were the most\ud visible and frequent sources and disseminators of Zika-related Twitter content. Grassroots users were the primary source and disseminators of conspiracy theories.\ud \ud Discussion & Conclusions: Social media analytics enable public health institutions to quickly learn what information is being disseminated, and by whom, regarding infectious diseases. Such information can help public\ud health institutions identify and engage with news media and other active information providers. It also provides insights into media and public concerns, accuracy of information on Twitter, and information gaps that\ud may exist. The study identifies implications for pandemic preparedness and response in the digital era and presents the agenda for future research and practice.

This study integrates network and content analyses to examine valence-based homophily on Twitter or the tendency for individuals to interact with those expressing similar valence. During the 2012 federal election cycle, we collected Twitter conversations about 10 controversial political topics and mapped their network ties. Using network analysis, we discovered clusters—subgroups of highly self-connected users—and coded messages in each cluster for their expressed positive-to-negative emotional valence, level of support or opposition, and political leaning. We found that valence-based homophily successfully explained the selection of user interactions on Twitter, in terms of expressed emotional valence in their tweets or support versus criticism to an issue. It also finds conservative voices to be associated with negatively valenced clusters and vice versa. This study expands the theory of homophily beyond its traditional conceptualization and provides a new understanding of political-issue interactions in a social media context.

This study proposes theoretical and practical frameworks to systematically examine mediated public relations in social media spaces. We applied a social network conceptual framework to identify and characterize social mediators that connect the US State Department with its international public. The results showed that social mediators vary in terms of their formality and interdependence. Formal social mediators were primarily US government agencies while informal social mediators were nongovernmental organizations (NGOs) and individuals. Notably, relationships with populations in the Middle East and North Africa were mediated primarily by informal actors, and formal mediators played a key role in connecting the public with everywhere else in the world. Government-related formal mediators and informal social mediators showed similar levels of bilateral relationships. In contrast, news media, the most traditional public relations mediators, were rarely found as social mediators and demonstrated the most uni...

This study takes a social networks approach to studying the Twitter talk evoked by politically oriented cable television hosts. Two aspects of user interactions are examined: the interconnectedness of users as an indication for users' networks of exchange of opinions and information, and exposure to a political diversity of information sources. Findings suggest that users prefer exposing themselves to politically like-minded information sources. Furthermore, television hosts failed to evoke an exchange of opinions and ideas among their followers. In contrast, other information sources that reported about these hosts evoked denser interaction among their followers.

The diffusion of social networking platforms ushered in a new age of peer-to-peer distributed online advertising content, widely referred to as viral advertising. The current study proposes a social networks approach to the study of viral advertising and identifying influencers. Expanding beyond the conventional retweets metrics to include Twitter mentions as connection in the network, this study identifies three groups of influencers, based on their connectivity in their networks: Hubs, or highly retweeted users, are Primary Influencers; Bridges, or highly mentioned users who associate connect users who would otherwise be disconnected, are Contextual Influencers, and Isolates are the Low Influence users. Each of these users’ roles in viral advertising is discussed and illustrated through the Heineken’s Worlds Apart campaign as a case study. Providing a unique examination of viral advertising from a network paradigm, our study advances scholarship on social media influencers and their contribution to content virality on digital platforms.

New media platforms such as Twitter allow users to choose from a wide range of political information sources including traditional media, politicians, bloggers and other Twitter users. This study extends the theory of channel complementarity by considering the complementary selection of information sources that occur within a single social media space. This study also conceptualizes information seeking as a social network created by follow relationships on Twitter, allowing tracking actual patterns of source selection. We applied this theoretical and methodological framework to examine information seeking on Twitter related to four US gubernatorial races. We identified the main types of highly followed accounts (i.e. information hubs). Traditional news media outlets (national and local) accounted for nearly half of the information hubs, and grassroots sources such as activists and other users accounted for nearly a third. The patterns of follow relationships indicated local and national subgroups (i.e. cl...

This study suggests taking a social networks theoretical approach to predict and explain patterns of information exchange among Twitter prostate and breast cancer communities. The authors collected profiles and following relationship data about users who posted messages about either cancer over 1 composite week. Using social network analysis, the authors identified the main clusters of interconnected users and their most followed hubs (i.e., information sources sought). Findings suggest that users who populated the persistent-across-time core cancer communities created dense clusters, an indication of taking advantage of the technology to form relationships with one another in ways that traditional one-to-many communication technologies cannot support. The major information sources sought were very specific to the community health interest and were grassroots oriented (e.g., a blog about prostate cancer treatments). Accounts associated with health organizations and news media, despite their focus on health, did not play a role in these core health communities. Methodological and practical implications for researchers and health campaigners are discussed.