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As human beings step into the age of information network, big data technology is constantly improving the intelligence level of various agents such as individuals and enterprises. The crowd decision-making of the intellectual community plays an important role in the active participation of many individuals and schools in giving their wisdom, effectively solve the problems of negative internet communication, single publicity media and unprofessional promotion team in WeChat public account.

This paper aims to optimize the content and improve the effectiveness of network ideological and political education in universities. This study analyzes five highly popular WeChat public accounts at the Central University of Finance and Economics in 2019. It obtains the popularity index of tweets using the WeChat communication index algorithm and finds that the important factors that influence tweet popularity are release time and content value.

To improve the public account tweets, this study highlights the connection between the tweets’ value and students’ emotional needs, which enhances the value of tweet content in students’ life and provides more original and distinctive content.

This study found that the content and interest of college students are tweet time, tweet value and tweet content. Therefore, the public account of college ideological and political education should be improved from the following three aspects: realizing the connection between the value of tweet content and students’ emotional needs; enhancing the value of tweet content in students’ life and learning; and insisting on the original and distinctive original intention of tweet content.

This paper aims to examine how and why consumers respond to chief executive officer (CEO) activism on social media. The authors developed a conceptual model that proposes impression management as a mechanism for consumer response to CEO activism.

In Study 1a, the authors examined 83,259 tweets from 90 CEOs and compared consumer responses between controversial and noncontroversial tweets. In Study 1b, the authors replicated the analysis, using a machine-learning topic modeling approach. In Studies 2 and 3, the authors used experimental designs to test the theoretical mechanism.

On average, consumers tend to respond more to CEO posts dealing with noncontroversial issues. Consumers’ relative reluctance to like and share controversial posts is motivated by fear of rejection. However, CEO fame reverses this effect. Consumers are more likely to engage in controversial activist threads by popular CEOs. This effect holds for consumers high (vs low) in public self-consciousness. CEO fame serves as a “shield” behind which consumers protect their online image.

The study focused on Twitter (aka “X”) in the USA. Future research may replicate the study in other social media platforms and countries. The authors introduce “shielding” – liking and sharing content authored by a recognizable source – as a tactic for impression management on social media.

Famous CEOs should speak up about controversial issues on social media because their voice helps consumers engage more in such conversations.

This paper offers a theoretical framework to understand consumer reactions to CEO activism.

Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.

Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.

Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.

This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

This chapter demonstrates a parallel between the presidential campaigns of Jair Bolsonaro (Brazil, 2018) and Donald Trump (United States, 2016) regarding their use of social media. Specifically, tweets from the former candidates on the social network sites were analyzed during a one-month timeframe before their respective presidential elections. Tweets were selected for analysis based on the fact that Twitter was the main platform used by both former presidential candidates. The analysis of the data reveals considerable similarities between the communication strategies of the two candidates. This research enlists McCombs and Shaw (1972) agenda setting theory based on their study of media during North American presidential campaigns in 1968 and Lippmann’s (2008) concept of public opinion. The methodology employed is based on Bardin (2011).

This chapter provides an overview of the specific legal, ethical, and privacy issues that can arise when conducting research using Twitter data. Existing literature is reviewed to inform those who may be undertaking social media research. We also present a number of industry and academic case studies in order to highlight the challenges that may arise in research projects using social media data. Finally, the chapter provides an overview of the process that was followed to gain ethics approval for a Ph.D. project using Twitter as a primary source of data. By outlining a number of Twitter-specific research case studies, the chapter will be a valuable resource to those considering the ethical implications of their own research projects utilizing social media data. Moreover, the chapter outlines existing work looking at the ethical practicalities of social media data and relates their applicability to researching Twitter.

This chapter examines how established media – that is, print, TV and radio sources which pre-existed the popularisation of social media – use social media to disseminate content. Specifically it examines the manner in which three UK media sources – BBC News, The Guardian and the Daily Mail – used Twitter during the 2014–2015 Ebola crisis. It asks five key questions concerning: the balance between factual reporting and opinion or comment; the degree to which it shifted attention to specific events within the context of the outbreak; whether the dialogical potential of social media was exploited; the degree to which social media acted as a signpost to more detailed information elsewhere, or existed as independent content; and the degree of media reflexivity. It concludes that established media used this new technology within their existing paradigms for reporting rather than exploiting some of its more innovative characteristics.

Do public opinion and political sentiments expressed on Twitter during election campaign have a meaning and message? Are they inferential, that is, can they be used to estimate the political mood prevailing among the masses? Can they also be used to reliably predict the election outcome? To answer these in the Indian context, the 2014 general election was chosen.

Tweets posted on the leading parties during the voting and crucial campaign periods were mined and manual sentiment analysis was performed on them.

A strong and positive correlation was observed between the political sentiments expressed on Twitter and election results. Further, the Time Periods during which the tweets were mined were found to have a moderating effect on this relationship.

This study showed that the month preceding the voting period was the best to predict the vote share with Twitter data – with 83.9% accuracy.

Twitter has become an important public communication tool in India, and as the study results reinstate, it is an ideal research tool to gauge public opinion.

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.