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An increasing number of users are inspired by enterprises to repost social media messages, which greatly contributes to the dissemination of such messages in an online social network. The purpose of this paper is to discover the repost patterns of users regarding enterprise social media messages to help enterprises improve information management abilities for social media.

This paper proposes a novel method to discover the repost patterns of users in enterprise social networking (ESN) at the macro-level through topic analysis. Specifically, it proposes the message-diversity metric to measure the latent topic diversity degree of the social media messages. Through this technique, the paper analyzes the message-diversity characteristics of the enterprise social media messages and then explores the repost patterns of users.

The experimental results show that a high repost rate is more prominent for the messages with diverse latent topics, where message-diversity is as high as 0.5.

The findings have great potential in several management areas, such as employing social media marketing, predicting popular messages, helping enterprises strengthen their online presence, and gathering more potential customers.

This study explores how the repost patterns of users in ESN can be determined through general macro-level behavior of users instead of their micro-level processes. The patterns can also lead to a deeper understanding of which contents can drive people to diffuse information. This study gives an important insight into the information behavior of social media users for enterprise management researchers.

The purpose of this paper is to introduce Digital Methods Initiative Twitter Capture and Analysis Toolset, a toolset for capturing and analyzing Twitter data. Instead of just presenting a technical paper detailing the system, however, the authors argue that the type of data used for, as well as the methods encoded in, computational systems have epistemological repercussions for research. The authors thus aim at situating the development of the toolset in relation to methodological debates in the social sciences and humanities.

The authors review the possibilities and limitations of existing approaches to capture and analyze Twitter data in order to address the various ways in which computational systems frame research. The authors then introduce the open-source toolset and put forward an approach that embraces methodological diversity and epistemological plurality.

The authors find that design decisions and more general methodological reasoning can and should go hand in hand when building tools for computational social science or digital humanities.

Besides methodological transparency, the software provides robust and reproducible data capture and analysis, and interlinks with existing analytical software. Epistemic plurality is emphasized by taking into account how Twitter structures information, by allowing for a number of different sampling techniques, by enabling a variety of analytical approaches or paradigms, and by facilitating work at the micro, meso, and macro levels.

The paper opens up critical debate by connecting tool design to fundamental interrogations of methodology and its repercussions for the production of knowledge. The design of the software is inspired by exchanges and debates with scholars from a variety of disciplines and the attempt to propose a flexible and extensible tool that accommodates a wide array of methodological approaches is directly motivated by the desire to keep computational work open for various epistemic sensibilities.

The purpose of this paper is to engage in a systematic analysis of academic research that relies on the collection and use of Twitter data, creating topology of Twitter research that details the disciplines and methods of analysis, amount of tweets and users under analysis, the methods used to collect Twitter data, and accounts of ethical considerations related to these projects.

Content analysis of 382 academic publications from 2006 to 2012 that used Twitter as their primary platform for data collection and analysis.

The analysis of over 380 scholarly publications utilizing Twitter data reveals noteworthy trends related to the growth of Twitter-based research overall, the disciplines engaged in such research, the methods of acquiring Twitter data for analysis, and emerging ethical considerations of such research.

The findings provide a benchmark analysis that must be updated with the continued growth of Twitter-based research.

The research is the first full-text systematic analysis of Twitter-based research projects, focussing on the growth in discipline and methods as well as its ethical implications. It is of value for the broader research community currently engaged in social media-based research, and will prompt reflexive evaluation of what research is occurring, how it is occurring, what is being done with Twitter data, and how researchers are addressing the ethics of Twitter-based research.

Food plays an important role in every culture around the world. Recently, cuisine preference analysis has become a popular research topic. However, most of these studies are conducted through questionnaires and interviews, which are highly limited by the time, cost and scope of data collection, especially when facing large-scale survey studies. Some researchers have, therefore, attempted to mine cuisine preferences based on online recipes, while this approach cannot reveal food preference from people’s perspective. Today, people are sharing what they eat on social media platforms by posting reviews about the meal, reciting the names of appetizers or entrees, and photographing as well. Such large amount of user-generated contents (UGC) has potential to indicate people’s preferences over different cuisines. Accordingly, the purpose of this paper is to explore Chinese cuisine preferences among online users of social media.

Based on both UGC and online recipes, the authors first investigated the cuisine preference distribution in different regions. Then, dish preference similarity between regions was calculated and few geographic factors were identified, which might lead to such regional similarity appeared in our study. By applying hierarchical clustering, the authors clustered regions based on dish preference and ingredient usage separately.

Experimental results show that, among 20 types of traditional Chinese cuisines, Sichuan cuisine is most favored across all regions in China. Geographical proximity is the more closely related to differences of regional dish preference than climate proximity.

Different from traditional definitions of regions to which cuisine belong, the authors found new association between region and cuisine based on dish preference from social media and ingredient usage of dishes. Using social media may overcome problems with using traditional questionnaires, such as high costs and long cycle for questionnaire design and answering.

This study empirically assesses the perceptions the public has of employees and their organization following a [re]tweet, and the additional potential ameliorating effect of a disclaimer distancing the organization from the individual employee's social media presence.

A fully crossed 2 (disclaimer vs. no disclaimer) × 2 (positive vs. negative valence post) × 2 (post vs. retweet) experiment exposed participants (N = 173) to an employee's personal tweet. Resultant perceptions of both the poster (i.e., goodwill) and the poster's organization (i.e., organizational reputation) were analyzed using planned contrast analyses.

Findings reveal audiences form impressions of individuals based on both tweeted and retweeted content. Perceptions of both the poster's goodwill and the poster's organization were commensurate with the valence of the poster's tweets, stronger when posts were original tweets rather than retweets, and there was a significant interaction effect between valence and [re]tweet. Disclaimers did not significantly affect perceptions, suggesting employers may be better served by asking employees to omit reference to their employer on their personal social media accounts.

This research contributes to understanding how employee and organizational reputation are affected by employees' personal social media content. Results suggest that even when a disclaimer explicitly seeks to distance an employee from the organization, audiences still see the employee as an informal brand ambassadors of their organization.

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.

Social media have increasingly gained credibility as information sources in emergencies. Retweeting or resharing nature has made Twitter a popular medium of information dissemination. The purpose of this article is to enhance our understanding of both linguistic style and content properties (i.e. both affective and informational contents) that drives resharing behavior or virality of disaster messages on Twitter. We investigate this issue in the context of natural disaster crisis.

In this study, the authors develop, drawing upon language expectancy and uncertainty reduction theories as an enabling framework, hypotheses about how the language (i.e. style and content) influence resharing behavior. They employ a natural language processing of disaster tweets to examine how the language – linguistic style (concrete and interactive language) and linguistic content (information- and affect-focused language) – affects resharing behavior on Twitter during natural disasters. To examine the effects of both linguistic style and content factors on virality, a series of negative binomial regressions were conducted, particularly owing to the highly skewed count data.

Our analysis of tweets from the 2013 Colorado floods shows that resharing disasters tweets increases with the use of concrete language style during acute emergencies. Interactive language is also positively associated with retweet frequency. In addition, neither positive nor negative emotional tweets drive down resharing during acute crises, while information-focused language content has a significantly positive effect on virality.

Agencies for public safety and disaster management or volunteer organizations involved in disseminating crisis and risk information to the public may leverage the impacts of the linguistic style and language content through the lens of our research model. The findings encourage practitioners to focus on the role of linguistic style cues during acute disasters. Specifically, from the uncertainty reduction perspective, using concrete language in the disaster tweets is the expected norm, leading to a higher likelihood of virality. Also, interactively frame disaster tweets are more likely to be diffused to a larger number of people on Twitter.

The language that people use offer important psychological cue to their intentions and motivations. However, the role of language on Twitter has largely been ignored in this crisis communication and few prior studies have examined the relationship between language and virality during acute emergencies. This article explains the complex and multifaceted nature of information resharing behavior using a multi-theoretical approach – including uncertainty reduction and language expectancy theory – to understand effects of language style and content cues on resharing behavior in the context of natural crisis events.

The aim of this study was to examine the early stages of the COVID-19 outbreak and the international communication management of Chinese diplomats as a case for extending the definition of intermestic public diplomacy. The goal was to reveal how Beijing subtly used both domestic and foreign social media to organize a network for communication about COVID-19 and purposefully soften the highly centralized and hierarchical political propaganda of the Communist Party of China (CPC).

Based on the literature on digital public diplomacy, the authors applied the existing concept of intermestic to Chinese politics in order to demonstrate the digitalization of public diplomacy, along with its forms and strategies under an authoritarian regime. A hybrid methodology combining quantitative network analysis and qualitative discourse analysis permits examination of China's intermestic online communication network dynamics, shedding light on how such an intermestic practice promoted Chinese values and power to international publics in the early stages of the COVID-19 crisis.

The authors’ findings extend the implications of intermestic public diplomacy from a democratic context to an authoritarian one. By analyzing the content of public diplomacy and para-diplomatic social media accounts in China and abroad at the beginning of the COVID-19 crisis, the authors outlined China's early crisis management, explaining its intermestic public diplomacy transmission modes and strategies. Moreover, the authors identified changes in the narrative strategies of Chinese diplomats and journalists during this process.

The findings of this study underline that Beijing established a narrative-making virtual communication structure for disseminating favorable Chinese strategic narratives and voices through differentiated communication on domestic and foreign social media platforms. Such intermestic communication strategies were particularly evident and even further weaponized by Beijing in its large-scale Wolf Warrior diplomacy in the spring of 2020. Thus, the study’s findings help readers understand how China digitalized its public diplomacy, its digital communication patterns and strategies.

On the one hand, geopolitical uncertainty and the popularity of social media have contributed to the evolution of the intermestic model of public diplomacy. This model allows actors to coordinate homogenous and differentiated communication practices to deploy their influence. On the other hand, the authors did not examine how intermestic audiences perceive and receive public diplomacy practices. In future studies, scholars should measure the agenda-setting capacity of diplomatic actors by examining the effects of such intermestic communication efforts.

Drawing on the cognitive-functional model of emotions and emotional contagion, the authors aim to examine the role of negative emotions in the diffusion of cancer tweets.

Using an integrated approach of social network and text analytics, the authors analyzed 142,883 cancer tweets from February to March 2018. The roles of negative emotions, emotional contagion, cancer themes and user influence on the diffusion of cancer tweets were examined.

Results indicated that cancer tweets expressing negativity and anger diffused more widely, while those expressing sadness or fear were less likely to diffuse. However, contrary to the authors’ expectation, cancer tweets expressing negative emotions (i.e. negativity, anger and fear) were less likely to arouse similar emotions among retweets, thus suggesting that emotions in cancer tweets were not as contagious as they seemed. Finally, user influence was the most important factor explaining the diffusion of cancer tweets, although cancer-related themes (i.e. affective, informative and social) had marginal effects on likelihood of diffusion.

Using a novel integrated social network–text analytics approach, the authors found that to understand cancer tweets' diffusion, it is critical to go beyond examining the content of tweets about cancer and the influence of messengers – the virality of cancer tweets is inextricable from the negative emotions.

The purpose of this paper is to apply Connolly’s (2003) concept of agonistic respect to develop a typology of agonistic/antagonistic discourses on Twitter. To develop the typology, this study examines 2,236 Tweets containing the hashtag #guncontrol and uses NodeXL (Smith et al., 2010) to create a network map from which the 75 most influential accounts are derived. Using constant-comparative analysis (Glaser and Strauss, 1967), the authors identify seven categories of discourse style based on Connoly’s (2001) notion of ressentiment and “good faith presentations” of opposing arguments: furtive/secretive, cravenly opportunistic, willfully ignorant, irrational sentimental, misunderstanding/misguided, contingently wrong and reciprocal inquiry. The typology provides a useful and unique way to operationalize agonistic democratic theory and serves as the possible basis for training a machine learning classifier to detect antagonistic discourses on social media platforms.

To determine the level of agonism on Twitter, the authors examine tweets that employed the hashtag #guncontrol on March 12, 2018, one month after the shooting at the Marjory Stoneman Douglass High School in Parkland, Florida on February 14. The authors used the NodeXL excel add-on to collect and map 2,236 tweets. Using grounded theory/constant-comparative analysis (Glaser and Strauss, 1967), the authors develop a typology of seven types of discourses ordered from most antagonistic to most agonistic using Connolly’s (1993) concept of agonistic respect.

After examining the top 75 most shared tweets and using constant-comparative analysis to look for patterns of similarity and dissimilarity, the authors identified seven different ways in which individuals present their opponents’ value positions on Twitter on the issue of gun control. The authors were guided by agonistic theory in the authors’ inquiry. The authors looked at how Twitter users expressed their opponent’s faith/value positions, how pluralistic the discourse space was in the comment threads and how much the “talk” was likely to elicit ressentiment from adversaries.

Because the authors intended to engage in theory building, the authors limited the analysis to a selected number of tweets on one particularly salient topic, on one day. The intent of this was to allow for a close reading of the tweets in that specific network for the purposes of creating a useful typology that can be applied to a broader range of cases/issues/platforms.

The authors hope that typology could serve as a potential starting point for Twitter to think about how it could design its algorithms toward agonistic talk. The typology could be used as a classification scheme to differentiate agonistic from antagonistic threads. An algorithm could be trained to spot threads overwhelmingly populated by antagonistic discourse and instructed to insert posts from other threads that represent agonistic responses like “contingently wrong” or “reciprocal inquiry.” While generous presentations or deeper, more nuanced presentations of the opponent’s value position are not a panacea, they could serve to change the orientation by which users engage with policy issues.

Social media platforms like Twitter have up to now been left alone to make markets and establish profitability off of public sphere conversations. The result has been a lack of attention to how discourse on these platforms affects users mental well-being, community health and democratic viability. Recently, Twitter’s CEO has indicated a need to rethink the ways in which it promotes “healthy discourse.” The utilitarian presumption that, left to our own devices, we will trial and error our way to the collective good does not account for the importance of others in refining one’s preferences, arguments and world views. Without an “other” to vet ideas and lead us toward becoming wiser, we are left with a Wyly antagonism that moves discourse further and further away from agonistic respect and toward antagonistic virtual struggle. Platforms that allow antagonistic talk that breeds ressentiment run the risk of irrevocably damaging democracy through poisoning its public sphere.

This paper is unique in providing a typology/framework for thinking about the types of “political talk” that exists on Twitter. By using agonistic political theory as a framework, the authors are able to establish some guiding principles for “good political talk” that acknowledges the incommensurability of value positions on issues like gun control. The typology’s emphasis on agonistic respect, ressentiment and generosity in the presentation of alternative value positions provides a starting point from which to map and catalog discourse on Twitter more generally and offers a normative model for changing algorithmic design.