Social Media Conversations Research Articles

When national identity meets conspiracies: the contagion of national identity language in public engagement and discourse about COVID-19 conspiracy theories

AbstractThere are growing concerns about the role of identity narratives in spreading misinformation on social media, which threatens informed citizenship. Drawing on the social identity model of deindividualization effects (SIDE) and social identity theory, we investigate how the use of national identity language is associated with the diffusion and discourse of COVID-19 conspiracy theories on Weibo, a popular social media platform in China. Our results reveal a pattern of identity communication contagion in public conversations about conspiracies: national identity language usage in original posts is associated with more frequent use of such language in all subsequent conversations. Users who engaged in discussions about COVID-19 conspiracies used more national identity expressions in everyday social media conversations. By extending the SIDE model and social identity theory to misinformation studies, our article offers theoretical and empirical insight into how identity–contagious communication might exacerbate public engagement with misinformation on social media in non-Western contexts.

The Impact of Social Media Salience on the Subjective Value of Social Cues

Like face-to-face interactions, evidence shows that interacting on social media is rewarding. However, the rewards associated with social media are subject to unpredictable delays, which may shape how they are experienced. Specifically, these delays might enhance the subjective desirability of social rewards and subsequent reward-seeking behavior by sensitizing people to the presence of such rewards. Here, we ask whether thinking about a recent social media post or conversation influences the subjective value of monetary and social rewards. Across two studies, we find that individuals who are thinking about a recent social media post are more likely to sacrifice small financial gains for the chance to see a genuine smile (a social reward) compared with those who are thinking about a recent conversation. This suggests that rather than satisfying social needs, thinking about social media interactions enhances the subjective value of social rewards, potentially explaining the incentive value of social media.

A prospective venue for Marketing is Social Media Marketing

Large corporations have taken benefit of social media marketing's potential as a forum for marketing and have used it to drive their ad campaigns. Marketers are more conscious than ever of the different social media options that are available to them and are moving much faster than ever to start new sustainability campaigns. Social marketing is the method of promoting a company and its products and services through social media and other networks. While propagating their culture, mission, or desirable tone, organizations can utilize social media marketing to communicate with their existing customers and entice new ones. With help of data analytics tools specifically designed for social media marketing, marketers may monitor the effectiveness of their campaigns. The way we interact with one another and how our society functions as a whole have both changed as a consequence of social networking. Entrepreneurs started to notice how popular social media platforms like Twitter, Facebook and Instagram are becoming more and more. They started utilizing these platforms for social media marketing purposes to advance their objectives. That's because these websites have the power to alter user behaviour. On the social media platforms, marketing professionals can employ a variety of strategies and techniques to catch users' attention and enhance the engagement of their content. Marketers can employ the particular geographic, demographic, and personal information that users can submit on a variety of social media networks to customize their messaging to what is most likely to be popular with consumers. Social media is one of the "biggest chances" a company has currently now just to reach out to prospective customers. The means of socialization are social media. By establishing a closer connection with the public, these new media gain the trust of the public. Social networking marketing has transformed into the new norm for many organizations since the beginnings of last year. Social networking marketing has advanced, as have the companies that use it. It is impossible to pay to be completely silent on social media if a rival is generating controversy with its services and products. It is equally astounding how quickly the social media phenomenon is expanding and growing. With publication came social media marketing first. In an attempt to boost visitors to their sites and preferably, revenue, businesses published their information on social media. Social networking, however, has expanded to be a lot more than simply a method for disseminating information. Companies increasingly utilize social networking sites in a wide range of ways. For instance, an organization that is curious in what customers have to say about its brand may monitoring social media conversations and respond to significant references. In order to determine how effectively a company is operating on social media, it would employ an analytics tool to examine at its visibility, engagement, and sales. Highly targeted social media advertisements would be used by a company looking to reach a certain population on a large scale. The term "social media management" is frequently used to refer to all of them. Numerous ways in which social media has altered business. It is a reality that because of the digital era in which we live, social media and internet marketing have a big influence on how people behave. The technology is evolving quickly, which has increased both supply and demand over time. Automation of all processes is the only way to deal with the significant changes. Despite the emergence of the new communications era, experts advise companies to keep employing conventional methods while also making the most of social media to promote their goals. This study analyzes social media and social media marketing principles as well as related topics like social media's development and advantages, its function and importance in marketing, and its social media marketing tactics. It also offers a broad perspective of marketing through social media in India.

Substance use and sentiment and topical tendencies: a study using social media conversations of youth experiencing homelessness

PurposeThis study investigates associations between Facebook (FB) conversations and self-reports of substance use among youth experiencing homelessness (YEH). YEH engage in high rates of substance use and are often difficult to reach, for both research and interventions. Social media sites provide rich digital trace data for observing the social context of YEH's health behaviors. The authors aim to investigate the feasibility of using these big data and text mining techniques as a supplement to self-report surveys in detecting and understanding YEH attitudes and engagement in substance use.Design/methodology/approachParticipants took a self-report survey in addition to providing consent for researchers to download their Facebook feed data retrospectively. The authors collected survey responses from 92 participants and retrieved 33,204 textual Facebook conversations. The authors performed text mining analysis and statistical analysis including ANOVA and logistic regression to examine the relationship between YEH's Facebook conversations and their substance use.FindingsFacebook posts of YEH have a moderately positive sentiment. YEH substance users and non-users differed in their Facebook posts regarding: (1) overall sentiment and (2) topics discussed. Logistic regressions show that more positive sentiment in a respondent's FB conversation suggests a lower likelihood of marijuana usage. On the other hand, discussing money-related topics in the conversation increases YEH's likelihood of marijuana use.Originality/valueDigital trace data on social media sites represent a vast source of ecological data. This study demonstrates the feasibility of using such data from a hard-to-reach population to gain unique insights into YEH's health behaviors. The authors provide a text-mining-based toolkit for analyzing social media data for interpretation by experts from a variety of domains.

Twitter data from the 2019–20 Australian bushfires reveals participatory and temporal variations in social media use for disaster recovery

Social media platforms have proved to be vital sources of information to support disaster response and recovery. A key issue, though, is that social media conversation about disasters tends to tail off after the immediate disaster response phase, potentially limiting the extent to which social media can be relied on to support recovery. This situation motivates the present study of social media usage patterns, including who contributes to social media around disaster recovery, which recovery activities they contribute to, and how well that participation is sustained over time. Utilising Twitter data from the 2019–20 Australian bushfires, we statistically examined the participation of different groups (citizens, emergency agencies, politicians and others) across categories of disaster recovery activity such as donations & financial support or mental health & emotional support, and observed variations over time. The results showed that user groups differed in how much they contributed on Twitter around different recovery activities, and their levels of participation varied with time. Recovery-related topics also varied significantly with time. These findings are valuable because they increase our understanding of which aspects of disaster recovery currently benefit most from social media and which are relatively neglected, indicating where to focus resources and recovery effort.

Toward an Anti-Caste and Feminist Vision of Transformative Justice: Analyzing Social Media Activism Against Sexual Violence

This article attempts to understand how ideas of justice are conceptualized within social media discourses around gender and sexual violence in India through an intersectional analysis of two case studies: the case of the rape and murder of a Hyderabad veterinarian and the List of Sexual Harassers in Academia, or LoSHA, which generated many conversations on sexual violence and justice on Twitter, Instagram, and Facebook. While a cursory reading of these two social media responses might see both as a rejection of due process and as seeking justice outside the structures of the judicial system, we argue that a closer reading of both is necessary to understand the different contours that these calls for justice have taken. In addition, we study how these two different social media conversations on sexual violence have centered the victim/survivor of sexual violence differently to understand how they engage with carcerality and anti-carceral politics. We conclude by attempting a definition of transformative justice based on anti-caste feminist interventions on social media, specifically drawing on the work of Dalit feminists, including those who created LoSHA.

Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research Questions

The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today’s living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 Tweets about exoskeletons that were posted in a 5-year period from 21 May 2017 to 21 May 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset.

Doing More with a DM: A Survey on Library Social Media Engagement

Objectives – This study sought to determine the role social media plays in shaping library services and spaces, and how queries are received, responded to, and tracked differently by different types of libraries. Methods – In April and May of 2021, researchers conducted a nine-question survey (Appendix A) targeted to social media managers across various types of libraries in the United States, soliciting a mix of quantitative and qualitative results on prevalence of social media interactions, perceived changes to services and spaces as a result of those interactions, and how social media messaging fits within the library’s question reporting or tracking workflow. The researchers then extracted a set of thematic codes from the qualitative data to perform further statistical analysis. Results – The survey received 805 responses in total, with response rates varying from question to question. Of these, 362reported receiving a question or suggestion via social media at least once per month, with 247 reporting a frequency of less than once per month. Respondents expressed a wide range of changes to their library services or spaces as a result, including themes of clarification, marketing, reach, restriction, collections, access, service, policy, and collaboration. Responses were garnered from all types of libraries, with public and academic libraries representing the majority. Conclusion – While there remains a disparity in how different types of libraries utilize social media for soliciting questions and suggestions on library services and spaces, those libraries that participate in the social media conversation are using it as a resource to learn more from their patrons and communities and ultimately are better situated to serve their population.

Stakeholder Insights in Alzheimer's Disease: Natural Language Processing of Social Media Conversations.

Social media data may be especially effective for studying diseases associated with high stigma, such as Alzheimer's disease (AD). We primarily aimed to identify issues/challenges experienced by patients with AD using natural language processing (NLP) of social media posts. We searched 130 public social media sources between January 1998 and December 2021 for AD stakeholder social media posts using NLP to identify issues/challenges experienced by patients with AD. Issues/challenges identified by ≥10% of any AD stakeholder type were described. Illustrative posts were selected for qualitative review. Secondarily, issues/challenges were organized into a conceptual AD identification framework (ADIF) and representation of ADIF categories within clinical instruments was assessed. We analyzed 1,859,077 social media posts from 30,341 AD stakeholders (21,011 caregivers; 7,440 clinicians; 1,890 patients). The most common issues/challenges were Worry/anxiety (34.2%), Pain (33%), Malaise (28.7%), Confusional state (27.1%), and Falls (23.9%). Patients reported a markedly higher volume of issues/challenges than other stakeholders. Patient posts reflected the broader scope of patient burden, caregiver posts captured both patient and caregiver burden, and clinician posts tended to be targeted. Less than 5% of the high frequency issues/challenges were in the "function and independence" and "social and relational well-being" categories of the ADIF, suggesting these issues/challenges may be difficult to capture. No single clinical instrument covered all ADIF categories; "social and relational well-being" was least represented. NLP of AD stakeholder social media data revealed a broad spectrum of real-world insights regarding patient burden.

Analyzing voter behavior on social media during the 2020 US presidential election campaign

Every day millions of people use social media platforms by generating a very large amount of opinion-rich data, which can be exploited to extract valuable information about human dynamics and behaviors. In this context, the present manuscript provides a precise view of the 2020 US presidential election by jointly applying topic discovery, opinion mining, and emotion analysis techniques on social media data. In particular, we exploited a clustering-based technique for extracting the main discussion topics and monitoring their weekly impact on social media conversation. Afterward, we leveraged a neural-based opinion mining technique for determining the political orientation of social media users by analyzing the posts they published. In this way, we were able to determine in the weeks preceding the Election Day which candidate or party public opinion is most in favor of. We also investigated the temporal dynamics of the online discussions, by studying how users’ publishing behavior is related to their political alignment. Finally, we combined sentiment analysis and text mining techniques to discover the relationship between the user polarity and sentiment expressed referring to the different candidates, thus modeling political support of social media users from an emotional viewpoint.

Menggunakan Paradigma Studi Kasus Kualitatif Interpretatif Online dan Offline Untuk Memahami Efektivitas Penerapan E-Procurement

The presence of social media allows researchers to consider new data collection and triangulation strategies. Positivist researchers take advantage of the internet's presence as a medium for collecting data. Meanwhile, interpretive qualitative researchers have not utilized the potential provided by social media to enrich data collection in order to deepen understanding of a topic being researched. By using the case of the effectiveness of the application of e-procurement in a district, the researcher conducted face-to-face interviews (offline) and also observed discussions on social media and content sharing about the implementation and use of government e-procurement in a district. This study uses an interpretative qualitative paradigm to describe how social media exchanges, uploads, and conversations can be used as a means of collecting data in interpretive qualitative research. The results of this study indicate that social media observation can be used as a research tool to increase understanding of a topic being studied and also as a triangulation medium. This study also proves that the use of social media in interpretive studies can reduce challenges in informant recruitment, access to research sites, and reduce bias in interviews. This research contributes to the understanding of a research topic using data from two environments.Keywords:Social media, interpretif study, e-procurement effectiveness, research paradigm

Bots Amplify and Redirect Hate Speech in Online Discourse About Racism During the COVID-19 Pandemic

Online talk about racism has been salient throughout the COVID-19 pandemic. Yet while such social media conversations reflect existing tensions in the offline world, the same discourse has also become a target for information operations aiming to heighten social divisions. This article examines Twitter discussions of racism in the first and sixth months since COVID-19 was accorded pandemic status by the World Health Organization and uncovers dynamic associations with bot activity and hate speech. Humans initially constituted the most hateful accounts in online conversations about racism in March, but in August, bots dominated hate speech. Over time, greater bot activity likewise amplified levels of hate speech a week later. Moreover, while discourse about racism in March primarily featured an organic focus on racial identities like Asian and Chinese, we further observed a bot-dominated focus in August toward political identities like president, Democrat, and Republican. Although hate speech targeting Asian groups remained present among racism discussions in August, these findings suggest a bot-fueled redirection from focusing on racial groups at the onset of the pandemic to targeting politics closer to the 2020 US elections. This work enhances understanding of the complexity of racism discussions during the pandemic, its vulnerability to manipulation through information operations, and the large-scale quantitative study of inorganic hate campaigns in online social networks.

The Role of Influential Actors in Fostering the Polarized COVID-19 Vaccine Discourse on Twitter: Mixed Methods of Machine Learning and Inductive Coding.

BackgroundSince COVID-19 vaccines became broadly available to the adult population, sharp divergences in uptake have emerged along partisan lines. Researchers have indicated a polarized social media presence contributing to the spread of mis- or disinformation as being responsible for these growing partisan gaps in uptake.ObjectiveThe major aim of this study was to investigate the role of influential actors in the context of the community structures and discourse related to COVID-19 vaccine conversations on Twitter that emerged prior to the vaccine rollout to the general population and discuss implications for vaccine promotion and policy.MethodsWe collected tweets on COVID-19 between July 1, 2020, and July 31, 2020, a time when attitudes toward the vaccines were forming but before the vaccines were widely available to the public. Using network analysis, we identified different naturally emerging Twitter communities based on their internal information sharing. A PageRank algorithm was used to quantitively measure the level of “influentialness” of Twitter accounts and identifying the “influencers,” followed by coding them into different actor categories. Inductive coding was conducted to describe discourses shared in each of the 7 communities.ResultsTwitter vaccine conversations were highly polarized, with different actors occupying separate “clusters.” The antivaccine cluster was the most densely connected group. Among the 100 most influential actors, medical experts were outnumbered both by partisan actors and by activist vaccine skeptics or conspiracy theorists. Scientists and medical actors were largely absent from the conservative network, and antivaccine sentiment was especially salient among actors on the political right. Conversations related to COVID-19 vaccines were highly polarized along partisan lines, with “trust” in vaccines being manipulated to the political advantage of partisan actors.ConclusionsThese findings are informative for designing improved vaccine information communication strategies to be delivered on social media especially by incorporating influential actors. Although polarization and echo chamber effect are not new in political conversations in social media, it was concerning to observe these in health conversations on COVID-19 vaccines during the vaccine development process.

‘Somehow, I don't see it for myself:' white identity politics and antirelationality in the US right’s response to covid-19

ABSTRACT The novel virus known as COVID-19 emerged in late 2019 and became a global pandemic in early 2020. The supposed origin of the virus in a ‘wet market’ in Wuhan, China led to the deployment of anti-Chinese discourse in the Trump administration’s treatment of the virus. The pre-eminent protective health response, masking, quickly became defined as un-American and as a threat to freedom among Trump supporters. Drawing on public conversations in print and social media in spring 2020, the connections are traced between the US right-wing reaction to the global pandemic and the deep structures of whiteness as an antirelational identity in racial capitalism. This US right’s reaction to COVID-19, specifically the identification of the virus as Chinese and mask refusal, makes up part of a wider effort to reassert whiteness as central to American national identity. This ill-conceived response to the pandemic reveals how whiteness works through antirelational identity politics: i.e. racism, the denial of interconnection, and the refusal of efforts toward collective well-being.

Language Politeness on Social Networks among Upper Secondary Students: An Analysis of Politeness Principles

Linguistic politeness can be translated through the choice of a good word and the way it is spoken in a particular situation based on its purpose, such as greeting, asking, directing, or influencing. This study analyses language politeness on social media sites among upper secondary school students using Leech's (1983) Politeness Principle. The research instruments were derived from social media sites, such as Whatsapp and Facebook. The study also used qualitative methods, namely observation and questionnaires, to obtain data. During the field study, interviews and the distribution of questionnaires to respondents online were conducted to obtain the primary data. The study involved 60 male and female upper secondary school students from SMK Senawang and SMK Seri Pagi, located in Seremban, Negeri Sembilan, as respondents. The findings showed that in social media conversations, upper secondary school students most often showed a maximum use of politeness at 33.13%, a maximum use of agreement at 24.23% and a maximum use of humility at 42.64%. The strategic use of linguistic politeness in face-to-face and online conversations is based on the six maxims (maxim of politeness, maxim of kindness, maxim of support, maxim of agreement, maxim of humility and maxim of sympathy) found in Leech's Politeness Principle (1983). This Principle is found to help identify language politeness strategies in speakers who are upper secondary school students.

MeVer NetworkX: Network Analysis and Visualization for Tracing Disinformation

The proliferation of online news, especially during the “infodemic” that emerged along with the COVID-19 pandemic, has rapidly increased the risk of and, more importantly, the volume of online misinformation. Online Social Networks (OSNs), such as Facebook, Twitter, and YouTube, serve as fertile ground for disseminating misinformation, making the need for tools for analyzing the social web and gaining insights into communities that drive misinformation online vital. We introduce the MeVer NetworkX analysis and visualization tool, which helps users delve into social media conversations, helps users gain insights about how information propagates, and provides intuition about communities formed via interactions. The contributions of our tool lie in easy navigation through a multitude of features that provide helpful insights about the account behaviors and information propagation, provide the support of Twitter, Facebook, and Telegram graphs, and provide the modularity to integrate more platforms. The tool also provides features that highlight suspicious accounts in a graph that a user should investigate further. We collected four Twitter datasets related to COVID-19 disinformation to present the tool’s functionalities and evaluate its effectiveness.

The Language of Brands in Social Media: Using Topic Modeling on Social Media Conversations to Drive Brand Strategy

This article highlights how social media data and language analysis can help managers understand brand positioning and brand competitive spaces to enable them to make various strategic and tactical decisions about brands. The authors use the output of topic models at the brand level to evaluate similarities between brands and to identify potential cobrand partners. In addition to using average topic probabilities to assess brands’ relationships to each other, they incorporate a differential language analysis framework, which implements scientific inference with multi-test-corrected hypothesis testing, to evaluate positive and negative topic correlates of brand names. The authors highlight the various applications of these approaches in decision making for brand management, including the assessment of brand positioning and future cobranding partnerships, design of marketing communication, identification of new product introductions, and identification of potential negative brand associations that can pose a threat to a brand's image. Moreover, they introduce a new metric, “temporal topic variability,” that can serve as an early warning of future changes in consumer preference. The authors evaluate social media analytic contributions against offline survey data. They demonstrate their approach with a sample of 193 brands, representing a broad set of categories, and discuss its implications.

Smart cities and citizen engagement: Evidence from Twitter data analysis on Italian municipalities

Smart cities are increasingly keen to establish a fruitful conversation with their citizens, to better capture their needs, and create virtual platforms for stimulating co-creation processes between government and users, with the final objective of increasing the quality of life and well-being. Social media applications provide an opportunity for dialogic communication, where, for a relatively low cost, a large amount of information reaching a wide audience can be published and exchanged in real time, fueling opportunities for citizens' engagement. This study is based on a social media listening method, through Twitter data mining, which enabled disentangling different components of citizen engagement (popularity, commitment, and virality) for a sample of Italian municipalities. In addition, we executed a deep analysis of the types of communication artifacts exchanged and, through a content analysis of the tweets published by followers of the municipalities' accounts, we identified the main areas of interest in the social media conversations. Our results are based on the analysis of online conversations engaged by followers of Twitter accounts of a sample of 28 Italian municipalities, chosen among the most active and densely populated. We show that municipalities tend to use the Twitter account as a channel of communication to inform the population about a variety of topics, such as transport and public works, among the others. The volume of activity and number of followers (audience) vary from one municipality to the other. There is generally a negative relationship between the density of the population of a municipality and citizens' engagement: smaller municipalities show a higher citizens' engagement; the biggest ones, like Roma, Milan, Turin, Naples, are laggards. We finally conducted a city profiling process, which provides a representation of key citizens' segments in terms of engagement. Policymakers could find in our work useful tools to increase citizens' listening capacity.

Testing the antecedents of customer knowledge sharing on social media: a quantitative analysis on Italian consumers

PurposeThe increasing adoption of digital technologies such as social media have changed the way consumers share knowledge about products and services among each other. The aim of this paper is to test what factors drive customers to share knowledge about products and services on social media pages.Design/methodology/approachA quantitative survey design was employed for this study. Empirical data were drawn from 358 consumers in Italy, using a purposive sampling technique. The hypothesised relationships were tested using ordinary least squares regression modelling.FindingsThe results of this study reveal that the usage frequency of online reviews (UFORs), social bonds (SBs), subjective happiness (SH) and reciprocity positively impact on customer knowledge sharing (CKS). By contrast, the perceived usefulness of online reviews (PUORs), helping others, customer susceptibility to interpersonal influence (CSII) and informational (INFO) do not impact CKS.Originality/valueTo the best of the authors' knowledge, this study is amongst the first to empirically test the antecedents of knowledge-sharing behaviours about products and services on online social media. The present work offers relevant implications for theory. First, the work enriches the customer knowledge management (CKM) theory by providing empirical evidence on factors leading to the higher sharing of knowledge amongst customers. Second, the work adds to the literature on social media, demonstrating the individual determinants on knowledge-sharing behaviours about products and services in online communities. Practically speaking, this paper identifies some key elements driving CKS in social media conversations. Thus, building upon the findings of this study, the authors provide some guidelines for social media managers and retailers for promoting CKS on social media pages.

273 IgA Nephropathy (IgAN) Symptoms and Disease Impact: A Linguistic Analysis of Social Media Conversations in IgAN with Patients, Caregivers, and Healthcare Providers

273 IgA Nephropathy (IgAN) Symptoms and Disease Impact: A Linguistic Analysis of Social Media Conversations in IgAN with Patients, Caregivers, and Healthcare Providers