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The academic literature reports mixed evidence on how social media platform and message impact consumer engagement. We investigated the effects of three platforms (Facebook, Instagram, and Twitter) and three message themes (sales, informational, and relationship building) on six consumer engagement actions (comment, like, search, share, talk about, and purchase) in a lab experiment. College students responded to social media posts featuring their National Collegiate Athletic Association Division I women's basketball team. Results for platform show that participants were more likely to comment on Facebook and Twitter (vs. Instagram) and more likely to purchase on Twitter (vs. Instagram). Results for message theme show that participants were more likely to comment, like, and share informational and relationship building posts and more likely to purchase after sales posts. Results for message theme vary by gender for search and talk about (with others). These results can help sport marketers develop social media content that drives specific engagement actions. [ABSTRACT FROM AUTHOR]

Marketers frequently create social media content (i.e., firm-generated content; FGC) to ignite interest in new movies. Thus, there is a clear need to understand the magnitude and heterogeneity of the effect of FGC on movie demand and associated user-generated content (UGC). The authors empirically examine the complex interactions among FGC, UGC, and sales using social media (tweet) data that are normally available to firms. They investigate two potential mechanisms by which FGC may drive box office revenues: (1) a direct mechanism, such that users who see FGC directly drive revenue, and (2) an indirect "ripple effect," by which FGC increases movie-related UGC, which then drives consumption. By analyzing 145,502 firm-generated and 5.9 million user-generated Twitter posts associated with 159 movies, the authors find a positive and significant effect of FGC on movie sales, which UGC fully mediates, which supports the indirect ripple effect reasoning. Impressions of FGC by followers of firm accounts, as opposed to nonfollowers of firm accounts, mainly drive the effect of FGC on UGC. In addition, FGC by movie accounts is more effective than that by actors and studios. Firms' regular posts with a movie-specific hashtag are more effective than replies, retweets, and posts without the hashtag. The finding of the ripple effect suggests that movie executives should focus on creating FGC that sparks conversations among followers when new movies are released. [ABSTRACT FROM AUTHOR]

The paper will investigate the complexities involved in moderating casteist content on major social media platforms. Focusing on the challenges of categorizing hate speech targeting India's marginalized caste-oppressed minorities, the research aims to delve into the mechanisms utilized by platforms for content moderation. It will critically analyze existing categorization systems, exploring the efficacy and limitations in identifying and addressing casteist comments. By leveraging information sciences concepts like grounded coding and taxonomies, the study will elucidate the complexities of classifying caste-related hate speech within these systems. Moreover, it will highlight the implications of inadequate categorization on content visibility and community impact.

As social media is increasingly used by communities to understand and cope with environmental hazards, understanding how people use social media before, during, and after disasters can support disaster response and recovery efforts. This paper presents an empirical application of Houston et al.’s (Disasters 39:1–22, 2015) functional framework for disaster social media, using the case of Twitter use during and after Hurricane María. Our research aims to (1) identify the predominant patterns of Twitter usage and content dissemination during the Hurricane María crisis and (2) validate and refine the functional framework for disaster social media with a case study of the hurricane that hit Puerto Rico in 2017. We find that people in the US used Twitter mainly to access news of the hurricane, express emotions (both negative and positive), and to understand socio-political events shaping the response and recovery. Most tweets came from individuals rather than organizations, and most were sent as the hurricane was designated as category four and approached Puerto Rico, with far fewer posts after landfall. These findings highlight the importance of individuals sharing and accessing vital information when official outlets are absent or limited and the relatively short-lived attention to slow recovery processes.

Aim Methods Results Conclusion The evolution of the utility of medical social media and its global reach has led to a much greater speed of dissemination of medical innovation, such as transanal total mesorectal excision (TaTME). The acceptability and discussions surrounding such innovations can be followed online. Here, we sought to determine if online discussions over time could match known models of innovation evolution using the example of TaTME since its initial description in 2010.Data on using the X hashtag #TaTME were analysed using the social media analytics tool Brandwatch. Trends in mentions, impressions and reach were highlighted over 13 years (1 May 2010 to 31 July 2023). Each mention's sentiment and emotional connotations were analysed using Brandwatch's natural language processing approach.We identified 18 525 mentions of #TaTME by healthcare professionals, reaching over 30.6 million X users. A Gartner's hype‐cycle‐like pattern was identified for mention volume and emotional connotation over time. The majority of mentions had a neutral (84%; n = 15 341) or positive connotation (15%, n = 2675). A peak of negatively connotated mentions was identified surrounding the temporary suspension of TaTME in 2019.This study presents a novel method of monitoring online discussions surrounding new surgical innovations by healthcare professionals based on sentiment analysis of public social media data. Mention volume and emotional connotation were the most accurate parameters closely resembling Gartner's hype cycle. [ABSTRACT FROM AUTHOR]

Social media has been shown to be an efficient way to engage in networked participatory scholarship (NPS), which is defined as the use of online social networks to share and further develop scholarship. As leaders in the field, educational technology scholars should be at the forefront of this practice. We used social network analysis (SNA) to examine the structure and characteristics of the #TPACK Twitter network and to determine whether and how the users were engaging in value‐added NPS. Our findings revealed that the #TPACK Twitter network was loosely organized, and users were not very well connected outside of their clusters. Our findings also revealed that #TPACK tweets largely did not represent value‐added NPS. The majority of posts lacked useful context, were limited to merely sharing links to resources and did not establish meaningful interactions among users. The implications of this study provide a new direction for educational technology researchers and PK‐12 practitioners to approach social media from a value‐added standpoint and to apply value‐added NPS to improve their use of social media to advance research, enhance professional learning and forge closer ties between researchers and practitioners. Practitioner notes What is already known about this topic Publication in high‐impact academic journals and measuring research impact through citations and journal indexing is still the dominant practice in research dissemination; however, there is growth in the use of alternative methods and the use of altmetrics to measure the impact of these methods. Scholars struggle with using social media in ways that align with networked participatory scholarship (NPS). Social network analysis (SNA) is the study of the structure and characteristics of the relationships that form in social networks, and can be used to fanalyze an online social network. What this paper adds Value‐added social learning theory and NPS can be used in SNA to both evaluate and inform scholars' social media practices. Educational technology scholars and practitioners struggle with using social media for value‐added NPS. Lost opportunities for value‐added NPS that were documented in this study include lack of engagement with the wider network, not sharing the role of spreading ideas and not making value‐added contributions. Implications for practice and policy Education scholars should use SNA as a tool to evaluate the level of value‐added NPS in the social media networks around areas of study they care about so that they can develop both personal and systemic plans for implementing value‐added NPS to create more dynamic learning spaces on these platforms. Institutions should investigate ways they can remove barriers and incentivize value‐added NPS. Innovations in the dissemination of scholarship should involve helping scholars to develop the skills needed for successful value‐added NPS. What is already known about this topic Publication in high‐impact academic journals and measuring research impact through citations and journal indexing is still the dominant practice in research dissemination; however, there is growth in the use of alternative methods and the use of altmetrics to measure the impact of these methods. Scholars struggle with using social media in ways that align with networked participatory scholarship (NPS). Social network analysis (SNA) is the study of the structure and characteristics of the relationships that form in social networks, and can be used to fanalyze an online social network. What this paper adds Value‐added social learning theory and NPS can be used in SNA to both evaluate and inform scholars' social media practices. Educational technology scholars and practitioners struggle with using social media for value‐added NPS. Lost opportunities for value‐added NPS that were documented in this study include lack of engagement with the wider network, not sharing the role of spreading ideas and not making value‐added contributions. Implications for practice and policy Education scholars should use SNA as a tool to evaluate the level of value‐added NPS in the social media networks around areas of study they care about so that they can develop both personal and systemic plans for implementing value‐added NPS to create more dynamic learning spaces on these platforms. Institutions should investigate ways they can remove barriers and incentivize value‐added NPS. Innovations in the dissemination of scholarship should involve helping scholars to develop the skills needed for successful value‐added NPS. [ABSTRACT FROM AUTHOR]

This article intervenes in debates about whether public-facing social media enable the rapid spread of hate speech, or whether these platforms can offer valuable opportunities to contest it. Advancing scholarship on 'networked counter-publics' and research emphasising the affective dimensions of digital media, we identify three different modes of counter-public contestation that coalesced on Twitter in the immediate aftermath of the Christchurch terrorist attack. Using a combined keyword and hashtag search, our research project sampled 3,099,138 tweets posted on/about the Christchurch attack and its repercussions, between 15 March 2019 and 15 April 2019. First, we examine two hashtags that trended, approaching these as nodal points for the construction of different affective responses to the terrorist attack. Second, we analyse instances where users quote-tweeted the condolences of politicians, rejecting their sentiments, arguing that the sincerity conditions of the Speech Act (condolence) were not met. Here, we focus on the ways that people invoke a discourse of indignation at either the past actions or current character of the politician, to justify rejecting their statements. Our findings illustrate a need to depart from broad narratives about how the affordances of particular social media platforms lend themselves to the circulation or contestation of hate. Instead, we argue, it is important to develop more situated empirical and conceptual approaches to interrogate how specific relationships between affective publics and structures of feeling enable or constrain political possibilities. [ABSTRACT FROM AUTHOR]

This study analyzes the distribution of content posted on Twitter in response to a specific event or crisis from 2014 to 2019. The aim of the study is to identify any shifts in the focus of the content and to explore the possible reasons for these changes. The findings suggest a shift from a disaster arena to a political arena over the six-year period. The initial years were dominated by content related to reporting on the situation, requesting help, and coordinating relief efforts, while the latter years saw an increase in content related to criticizing the government, appreciating government effort, and discussing social and political issues. The study provides insights into the changing nature of public responses to events and crises, and highlights the role of social media as a platform for political discussions. [ABSTRACT FROM AUTHOR]

Social media virality is intertwined with content's ability to trigger specific reactions; however, little is known regarding the behavioural component of political information diffusion. This study uses big data to investigate the significance of hyperactive social media use in the retransmission of information produced by political opinion leaders on Twitter during Poland's 2019 European parliamentary election campaign. Using the isolation forest method, the research identified the social bot-like (semi)automated or human-controlled Twitter handles which are referred to as hyperactive accounts. The study finds that the hyperactive accounts produced almost all reactions in our sample (N = 114,036), but their average activity was relatively moderate and did not exceed human capabilities. Although their retweets equal 0.46% of the retweets obtained by political opinion leaders, the random forest regression models suggest that the likes and retweets from hyperactive accounts are the most important determinants of the dissemination of political opinion leaders' tweets. The results draw attention to the alarming phenomenon of the further increase in Twitter's interactional asymmetry. [ABSTRACT FROM AUTHOR]

Social media platforms have revolutionized traditional communication techniques by allowing people to connect instantaneously, openly, and frequently. As people use social media to share personal stories and express their opinions, negative emotions such as thoughts of death, self-harm, and hardship are commonly expressed, particularly among younger generations. Accordingly, the use of social media to detect suicidality may help provide proper intervention that will ultimately deter the spread of self-harm and suicidal ideation on social media. To investigate the automated detection of suicidal thoughts in Arabic tweets, we developed a novel Arabic suicidal tweet dataset, examined several machine learning models trained on word frequency and embedding features, and investigated the performance of pre-trained deep learning models in identifying suicidal sentiment. The results indicate that the support vector machine trained on character n-gram features yields the best performance among conventional machine learning models, with an accuracy of 86% and F1 score of 79%. In the subsequent deep learning experiment, AraBert outperformed all other machine and deep learning models with an accuracy of 91% and F1-score of 88%, significantly improving the detection of suicidal ideation in the dataset. To the best of our knowledge, this study represents the first attempt to compile an Arabic suicidality detection dataset from Twitter and to use deep learning to detect suicidal sentiment in Arabic posts. [ABSTRACT FROM AUTHOR]

This paper investigates how war correspondents working for U.S.-based news organizations Tweeted about the early stages of the 2022 war in Ukraine, focusing particularly on instances when these war reporters contributed to a distinctly transnational version of what Matt Carlson has termed "metajournalistic discourse" (2016). Defining this concept as the "public expressions evaluating news texts, the practices that produce them, or the conditions of their reception" (Carlson [2016]. "Metajournalistic Discourse and the Meanings of Journalism: Definitional Control, Boundary Work, and Legitimation." Communication Theory 26 (4): 349–368 , 353), we argue that from February to May of 2022, U.S. war correspondents constructed a discourse that situated their own labor within the boundaries of what counts as the most acceptable form of war journalism, representing their reportage as the most independent and transparent form of war reporting. Conversely, they situated the work of Russian and Ukrainian journalists outside this boundary. The paper ultimately argues that journalism scholars should think more transnationally about the discourses that discuss journalistic labor. [ABSTRACT FROM AUTHOR]

This article explores three cases of celebrity miscarriages or pregnancy losses as mediated on Twitter. Through an exploration of how ambiguous grief is mediated in social media, this paper highlights the way gender, race, celebrity, and grief intersect in social media spaces. Using interdisciplinary work from media studies, gender studies, disability studies and trauma studies, this article offers a way to recognize how digital media offers space for coping to everyday people through mediations about and by celebrities, even while policing the boundaries of those celebrities' grief. In exploring the complexity of this type of digital mediation this article offers an entry point through which to understand other digital mediations of loss, grief, and trauma in social media. [ABSTRACT FROM AUTHOR]

The rapid onset of COVID-19 created a desire for information about the virus and government-directed response efforts. This study explores the sharing of Twitter posts from U.S. government officials and organizations during the onset of the COVID-19 pandemic. This study followed two paths of investigation. First, significant differences in the retweet frequency of government sources were determined based on the source (public officials and agencies, Republican- or Democrat-led states) and differentiated by equal time periods (40 days pre- or post-pandemic declaration). The study findings show that the frequency at which government accounts were retweeted in the U.S. was significantly impacted by the pandemic period, with the post-pandemic period significantly more active as governments wrestled with unprecedented economic, policy, and societal concerns related to the crisis. A greater reliance on public officials as opposed to public agencies or departments was seen, as was a greater tendency to retweet the posts of government officials from Democrat-led states. The second path examined the content of government tweets to determine the linguistic factors which predict retweet frequency. Study results found that both positive and negative emotional language in tweets predicted the frequency of retweeting government sources, though the results varied based on the source. The study results provide insight into the government-affiliated information sources users chose to share with their networks as well as the types of language that appeal to citizens during times of crisis. [ABSTRACT FROM AUTHOR]

Robotised propaganda has become a new stage in the dissemination of manipulative content online, exploited for its low entry costs and high effectiveness. State actors, non-state actors, and politically and business-focused actors can spread ideas, share thoughts or sow half-truths and lies with the help of hundreds of automatised accounts and contribute to the manipulation of people's minds. In a short time, robotised propaganda gained the attention of academics as well as international organisations. Especially, Russian influence operations utilising the newest advantages of technological development stand at the epicentre of research interests in the Central and Eastern European region. In our research, we aim to test the hypothesis that information from media which are presented as pro-Russian were connected to a higher percentage of accounts classified as bots. The research sample covers all the media categories in the Czech Republic, as stated by the Political Capital study 'The Weaponization of Culture: Kremlin's traditional agenda and the export of values to Central Europe'. For robotised accounts activity analysis, we use a script based on a tweetbotornot R package by Kearney. As the results show, pro-Russian positive media have a statistically significant higher probability for bot account activity than media expressing neutral opinions. [ABSTRACT FROM AUTHOR]

Social media use is an integral part of people's daily routine nowadays. Obviously, individuals with visual impairment are not excluded and use social media as well, even though they still face accessibility issues. The purpose of this review was to provide an overview of the existing literature on social media used by individuals with visual impairment and of the ways social media have been conceptualized throughout it. A literature search took place in the databases EBSCOhost, ERIC, Scopus and ResearchGate as well as the search engine Google Scholar. A total of 13 studies, published from 2011 to 2022 met our criteria and were included in this scoping review. According to our results, Facebook is the most used social media website by individuals with visual impairment as it was referenced in almost every single one of the studies reviewed and was recognized as the most used by individuals with visual impairment in the majority of them. Twitter is the second most popular social media website among individuals with visual impairment, followed by YouTube, WhatsApp, LinkedIn, Instagram, and Skype. Consequently, the statement that Facebook is the most popular social media website based on the number of its users is also confirmed with regard to users with visual impairment. However, the fact that an instant messaging service, such as WhatsApp, is recognized among researchers as a social medium when various social media definitions in the existing literature exclude this type of services showcases the confusion prevailing among researchers in this field. Thus, the literature would greatly benefit if researchers proposed a more thorough and robust definition for the widely, often inaccurately, used term social media. [ABSTRACT FROM AUTHOR]

Twitter is one of the well-known text-based social media that is often used to disseminate content. According to Katadata, Indonesia ranked fifth in the world in 2023. So many people or organizations want to make tweets go viral. Therefore, this research aims to develop a model that uses tweet data from the Indonesian language Twitter social media to categorize the level of virality. There are several tasks in classifying the level of virality, such as upsampling data, predicting sentiment and emotion, and text embedding. Upsampling data was carried out because the dataset used was an imbalanced dataset. Data upsampling, emotions, and text embedding is carried out using the bidirectional encoder representation from transformers (BERT) model. Meanwhile, sentiment prediction uses the Ro- bustly optimized BERT pretraining approach (RoBERTa). The results of text embedding, sentiment, emotion, will be combined with Twitter metadata then all features will be fed into the multi-layer perceptron (MLP) model to classifying the level of virality which is divided into 3 classes based on the number of retweets, namely low, medium and high. The proposed method produces an F1-score of 49% and an accuracy of 95% and performs better than the baseline model. [ABSTRACT FROM AUTHOR]

With the extensive technological advancements and expansion, the persistent issues regarding the creation and rapid dissemination of fake news have become a prevalent and recurrent concern. The manipulation of news content has critical repercussions, such as causing public mistrust, fear, harm, and misinformation. Addressing that, this study developed a supervised machine learning algorithm that can accurately classify social media data as fake news. The methodology of the proposed fake news detection model involved five main components: data acquisition from Twitter, data preprocessing, data transformation, model development using Naïve Bayes, decision tree, and support vector machine (SVM) and model evaluation using accuracy, precision, recall and F1-score. The results revealed that decision tree recorded the highest accuracy for both textual data (100%) and metadata (94.54%) and consistently outperformed both Naïve Bayes and SVM in terms of precision, recall, and F1-score metrics, with a score of 100% for the classification of textual data-based datasets. Regarding the metadata-based classification, decision tree also demonstrated excellent performance, with the highest F1-score of 94% for fake news data. Meanwhile, SVM exhibited the highest precision and recall performance for the metadata-based classification. Overall, the application of the decision tree classifier was deemed the most effective in Twitter fake news detection. [ABSTRACT FROM AUTHOR]

Studies of online campaigning have consistently demonstrated a positive impact on electoral success, but it remains unclear how this occurs. Some find the content and style of post matter, while others have pointed to overall activity as the key driver, promoting a “broadcast” effect model. Still, others have argued for indirect effects whereby candidates rely on followers to share content within their networks. This paper develops a “joined-up” model to test these arguments that includes new measures to capture the responsiveness of candidate tweets and the extent of user engagement. We apply the model to the 2017 UK General Election Twitter campaign. Our findings confirm a digital campaign effect, but in a two-step rather than direct manner. Specifically, candidates that attract more engagement with their tweets (likes and retweets) enjoy more electoral success. We expand on our findings to argue for a “network” rather than “broadcast” model of digital campaign effects. [ABSTRACT FROM AUTHOR]

In recent years, social media platforms, particularly X (Twitter), have become battlegrounds for political ideologies. Notably, certain European authorities have chosen to share anti-feminist messages on this platform. To effectively counter these discourses, understanding their construction is crucial. Thus, this research aims to analyse how far-right representatives in the European Parliament address feminism and gender equality on X. Using a mixed and exploratory approach, nearly 1.1 million messages were evaluated, revealing limited engagement by MEPs in gender-related topics. Five main discursive patterns were identified, offering valuable insights for developing targeted communication strategies to challenge these argumentative lines. Additionally, a connection was found between the negativity of tweets and their reach and engagement. [ABSTRACT FROM AUTHOR]

A disaster is an unexpected event with negative consequences for individuals and societies. Typically it is interfering with a community's or society's ability to function. Disasters can be man-made events, accidental events, or a natural catastrophes. These events create emergencies that require rapid responses. Timely information about the emergencies has to be obtained so that the responses can aid in dealing with the emergencies. One source of timely information about disasters is provided by social media postings which often provide on-site information about a disaster. An ideally suited social media tool for disaster information is Twitter due to the sort message format. This format enables the rapid composition of short messages by entities close to a disaster describing the nature of the disaster. The contents of the messages can then be used to guide emergency responses. The aim of this paper is to review the research on the usage of Twitter for emergency management that has been published so far. There are three steps required when using messages for information about disasters. The first step is to collect the data contained in the messages. Then the data has to be preprocessed for unification of format, duplication etc. Finally relevant information has to be extracted. These steps are considered in this paper reviewing the use of Twitter for emergency management. Papers using Twitter published within the past 5 years have been included. [ABSTRACT FROM AUTHOR]

Social media is very important to control the development of issues that occur today. With social shifts and changing societal values, polygamy has become a complex issue and attracts the attention of many people around the world discussed through social media platforms. This research contributes to the field by applying a sentiment analysis approach to automatically detect and analyze public sentiment regarding polygamiy content on Twitter, particularly in the context of Islamic-Muhammadiyah views. This study used decision tree classification methods, support vector machines, and random forests with the best analysis accuracy obtained at SVM 77.4%. Furthermore, the results of the sentiment class obtained were analyzed according to the views of Muhammadiyah. The results obtained in the analysis 77% commented negatively and 23% commented positively. In addition, this research can be used as a reference for future research on sentiment analysis cases to training and testing classroom models. [ABSTRACT FROM AUTHOR]

Exploring online criticisms of the "take the knee" protest during "Euro 2020", this article examines how alt- and far-right conspiracies were both constructed and communicated via the social media platform, Twitter. By providing a novel exploration of alt-right conspiracies during an international football tournament, a qualitative thematic analysis of 1,388 original tweets relating to Euro 2020 was undertaken. The findings reveal how, in criticisms levelled at both "wokeism" and the Black Lives Matter movement, anti-white criticisms of the "take the knee" protest were embroiled in alt-right conspiracies that exposed an assumed Cultural Marxist, "woke agenda" in the tournament's organisation and mainstream media coverage. In conclusion, it is argued that conspiratorial discourses, associated with the alt-right, provided a framework through which the protest could be understood. This emphasises how the significance of conspiracy functions to promote the wider dissemination of alt-right ideology across popular cultural contexts, such as sport. [ABSTRACT FROM AUTHOR]

Social media have reinvigorated the shift of focus from a party-centered politics to a personality-based one. This has made how politicians personalize on social media, especially in government, consequential for them and their administration. Vice presidents across the globe, especially in the United States, play crucial constitutional roles central to the country's political discourse. Though they are ignored in the extant literature, their rhetoric has ramifications for contemporary democracy. Using a multimodal rhetorical approach, I analyze Vice President Kamala Harris's tweets (n = 357) in her first year of office to understand how she constructs her public image through personalization. Results indicated that she displayed strong family bonds, reflected on her education and its influence, her historical accomplishments, as well as her emotional moments. I discuss the implications of the image she constructs in this present study. [ABSTRACT FROM AUTHOR]

Hesitant attitudes have been a significant issue since the development of the first vaccines—the WHO sees them as one of the most critical global health threats. The increasing use of social media to spread questionable information about vaccination strongly impacts the population's decision to get vaccinated. Developing text classification methods that can identify hesitant messages on social media could be useful for health campaigns in their efforts to address negative influences from social media platforms and provide reliable information to support their strategies against hesitant-vaccination sentiments. This study aims to evaluate the performance of different machine learning models and deep learning methods in identifying vaccine-hesitant tweets that are being published during the COVID-19 pandemic. Our concluding remarks are that Long Short-Term Memory and Recurrent Neural Network models have outperformed traditional machine learning models on detecting vaccine-hesitant messages in social media, with an accuracy rate of 86% against 83%. [ABSTRACT FROM AUTHOR]

Social media communication makes visible the linkages between governments' actions to diplomatic events and domestic audiences' reactions. This study analyses tweets that discuss the recent diplomatic crisis between Japan and Korea. Through the detection of clusters of Twitter users and the content analysis of the tweets in the two countries, this study shows the public's interpretation of a crisis widely differs by their stances towards their country's government. The analysis finds that the clusters are aligned with their pro- and anti-stances towards the current government and that pro-government clusters tend to interpret the diplomatic crisis through a historical perspective, while anti-government clusters interpret it as a matter of present-day politics. Furthermore, we find strong negativity in the tweets discussing the opponent online groups, especially by the anti-government cluster against the pro-government online camp. These findings suggest that a diplomatic crisis may create or deepen domestic polarisation. [ABSTRACT FROM AUTHOR]

Political partisanship constitutes a pivotal group identity that significantly influences individuals' voting behaviors and shapes their ideological and cultural perspectives. While traditional surveys and experimental studies can directly capture political identity by asking the participants, this task has become intricate when employing digital trace data sourced from social media. Previous classification methods, attempting to infer political identity from users' networks or textual content, suffered from limited efficiency or generalizability. In response, this study introduces a two-step method that utilizes deep learning models to enhance classification efficiency, generalizability, and interpretability. In the first step, two deep learning models, trained on 2.5 million tweets from 825 Congressional politicians in the U.S., achieved accuracy rates of 87.71% and 89.54%, respectively, in detecting politicians' partisanships based on their individual tweets. Subsequently, in the second step, by employing a simple machine learning model that leverages the aggregated predicted values derived from the first-step models, accuracy rates of 94.92% and 96.61% were attained for identifying non-politician users' political identities based off their 50 and 200 tweets, respectively. In addition, an attention mechanism was integrated into the deep learning model to assess the contribution of each word in the classification process. [ABSTRACT FROM AUTHOR]

Nowadays, various applications across industries, healthcare, and security have begun adopting automatic sentiment analysis and emotion detection in short texts, such as posts from social media. Twitter stands out as one of the most popular online social media platforms due to its easy, unique, and advanced accessibility using the API. On the other hand, supervised learning is the most widely used paradigm for tasks involving sentiment polarity and fine-grained emotion detection in short and informal texts, such as Twitter posts. However, supervised learning models are data-hungry and heavily reliant on abundant labeled data, which remains a challenge. This study aims to address this challenge by creating a large-scale real-world dataset of 17.5 million tweets. A distant supervision approach relying on emojis available in tweets is applied to label tweets corresponding to Ekman's six basic emotions. Additionally, we conducted a series of experiments using various conventional machine learning models and deep learning, including transformer-based models, on our dataset to establish baseline results. The experimental results and an extensive ablation analysis on the dataset showed that BiLSTM with FastText and an attention mechanism outperforms other models in both classification tasks, achieving an F1-score of 70.92% for sentiment classification and 54.85% for emotion detection. [ABSTRACT FROM AUTHOR]

Rising expectations in society for moral brand behavior have increased the relevance of brand purpose communication on social media. Nevertheless, there is a lack of fundamental research in this area. This study therefore examines brand purpose communication on Twitter, particularly with regard to the values addressed and the communication strategies implemented by corporate brands as well as the corresponding user engagement. A quantitative content analysis of 30 corporate Twitter accounts from 10 industries (n = 6000) shows that brand purpose is communicated across all brands and industries. The values of solidarity and sustainability are in the foreground. In an industry comparison, the FMCG industry and the financial services industry communicate most frequently on brand purpose issues, whereas the media industry and the luxury goods industry do so only marginally. Corporate brands predominantly use information strategy to communicate their brand purpose, although the involvement strategy leads to more engagement. Overall, the analysis shows that the potential of brand purpose communication is not being exploited to the same extent in all industries. [ABSTRACT FROM AUTHOR]

This study explores the perceptions of the Undetectable = Untransmittable (U = U) campaign among gay men with HIV in Japan who are part of the HIV community on Twitter. We conducted detailed semistructured interviews with 12 participants using a modified grounded theory approach. The analysis resulted in 31 concepts, 5 subcategories, 9 categories, and 2 core categories. The relationship between categories and core categories was examined in conjunction with the transcriptions, yielding a comprehensive category map depicting the process of understanding U = U. The three main processes emerged: participants expressed "expectations for improvement," underwent stages of "understanding U = U," and encountered "dilemmas" in forming their opinions. There are two routes to "understanding U = U": Route 1 involved a frictionless understanding of U = U, and Route 2 involved difficulty in understanding the slogan. In both routes, participants experienced the dilemma of "but I still have HIV" and regarding its ease of use. These dilemmas and difficulties in understanding U = U affected their "expectations for improvement." Efforts to promote U = U in Japan must address challenges in public understanding and related dilemmas. This entails reformulating the U = U message in simpler terms, leveraging professional explanations, disseminating it to a broader audience, and incorporating ideas from individuals living with HIV to enhance the message. [ABSTRACT FROM AUTHOR]

Television series enhance the social visibility of their content, as is the case with Queen Elizabeth II and The Crown. Netflix is the streaming television platform that has turned Peter Morgan's successful series (2016) into a television icon where the monarch is the main protagonist, taking us on a biographical journey that mixes the historical and the political with fiction. The main character is made to seem more humane and is brought closer to the general public, all of which leads to a transmedia narrative. This research aims to analyze the content of the messages published on Twitter during the days surrounding the Queen's State funeral in September 2022 and their connection with the series through the hashtag #TheCrown. The topics that have become trends worldwide are quantitatively analyzed, using different digital tools. The sample collected 1,489,279 tweets published during the days from the announcement of the death of Elizabeth II to the day of her funeral (from 8 to 19 September 2022). The results show nodes of connection between different players and linked communities to #TheCrown while offering the traffic generated by the hashtag with different nodes and edges. [ABSTRACT FROM AUTHOR]

The importance of social media has seen a dramatic increase in recent times, but much about its influence in academia is still unknown. To date, no comparative studies analysing the effect of social media promotion on citation counts have been undertaken in neurosurgical publishing. We randomized 177 articles published in Acta Neurochirurgica from May to September 2020. The 89 articles in the intervention group received a standardized social media promotion through one post on our official Twitter/X account, whereas the 88 articles in the control group did not receive any social media promotion. Citation counts, website visits and PDF downloads were tracked at one and two years post-promotion. We found no significant difference in number of citations at one year post-promotion (Intervention: 1.85 ± 3.94 vs. Control: 2.67 ± 6.65, p = 0.322) or at two years (5.35 ± 7.39 vs. 7.09 ± 12.1, p = 0.249). Similarly, no difference was detected in website visits at one (587.46 ± 568.04 vs. 590.65 ± 636.25, p = 0.972) or two years (865.79 ± 855.80 vs. 896.31 ± 981.97, p = 0.826) and PDF downloads at one (183.40 ± 152.02 vs. 187.78 ± 199.01, p = 0.870) or two years (255.99 ± 218.97 vs. 260.97 ± 258.44, p = 0.890). In a randomized study, a structured promotion of general neurosurgical articles on Twitter/X did not significantly impact citation count, website visits, or PDF downloads compared to no social media promotion. Combined with published evidence to date, the impact of social media on citation counts in academic publishing ultimately remains unclear. [ABSTRACT FROM AUTHOR]

This study aims to understand public reactions to statements about racial discrimination made by elected officials of color on Twitter. We argue that statements about race made by elected officials of color will be overlooked in most political contexts. However, during events when race is made a salient issue, the public will look to elected officials of the afflicted group for guidance and, through this, the public will be more likely to interact with these leaders on Twitter. In this way, periods of crises create a window of opportunity when statements against racism made by elected officials of color receive greater public attention. To assess this hypothesis, we use the case study of Asian American members of Congress and their discussion of anti-Asian racism on Twitter. We examine the pattern of Twitter interactions to their posts in 2021. We find that Asian American members of Congress receive more social media attention (likes and retweets) relative to other members of Congress when condemning anti-Asian violence, but only in the immediate wake of the March 2021 mass shooting of Asian Americans in Atlanta, Georgia. This study offers important new insight into when elected officials of color can draw public attention to the communities that they descriptively represent. [ABSTRACT FROM AUTHOR]

Background: There is sustained interest in understanding the perspectives of liver transplant recipients and living donors, with several qualitative studies shedding light on this emotionally charged subject. However, these studies have relied primarily on traditional semi-structured interviews, which, while valuable, come with inherent limitations. Consequently, there remains a gap in our comprehension of the broader public discourse surrounding living liver donation. This study aims to bridge this gap by delving into public conversations related to living liver donation through a qualitative analysis of Twitter (now X) posts, offering a fresh perspective on this critical issue. Methods: To compile a comprehensive dataset, we extracted original tweets containing the hashtags "#donateliver" OR "#liverdonor", all posted in English from January 1, 2012, to December 31, 2022. We then selected tweets from individual users whose Twitter (X) accounts featured authentic human names, ensuring the credibility of our data. Employing Braun and Clarke's reflexive thematic analysis approach, the study investigators read and analysed the included tweets, identifying two main themes and six subthemes. The Health Policy Triangle framework was applied to understand the roles of different stakeholders involved in the discourse and suggest areas for policy improvement. Results: A total of 361 unique tweets from individual users were analysed. The major theme that emerged was the persistent shortage of liver donors, underscoring the desperation faced by individuals in need of life-saving liver transplants and the urgency of addressing the organ shortage problem. The second theme delved into the experiences of liver donors post-surgery, shedding light on a variety of aspects related to the transplantation process, including the visibility of surgical scars, and the significance of returning to physical activity and exercise post-surgery. Conclusion: The multifaceted experiences of individuals involved in the transplantation process, both recipients and donors, should be further studied in our efforts to improve the critical shortage of liver donors. [ABSTRACT FROM AUTHOR]

Drawing on a broader study on perceptions of time and well-being during Covid-19, we show how governments can use social media platforms, such as Twitter, to acquire knowledge for policy learning and design. We argue social knowledge, which includes personal storytelling, emotion, and use of hashtags and emojis, can contribute to policy learning. Using a qualitative approach, we examine citizens' pandemic-related experiences, including changing work routines, mental health and self-care, sleep patterns, domestic violence, and feelings of solidarity. Such data could be useful to policymakers as they provide insights into the impact of the pandemic on citizens' everyday lives. [ABSTRACT FROM AUTHOR]

Social media, like Twitter, allows users to express opinions, comment, and share information freely. It has become a primary platform for fans to show support but also a space where verbal aggression can occur, influencing fan behavior and attitudes. This study examines the impact of the Twitter account @idextratime and verbal aggression on the fanaticism of Manchester United fans. Using a quantitative research method, a survey was conducted. Data analysis included descriptive analysis, classical assumption tests, multiple linear regression, and hypothesis testing (T-Test & F-Test).This research aims to determine how much influence each independent variable, namely social media and verbal aggression, has on the dependent variable, namely fanaticism, and how much influence the social media variable and verbal aggression simultaneously have on the fanaticism variable. Results showed significant influence, with social media and verbal aggression accounting for 88.4% of the variance in fanaticism. The study concluded that social media and verbal aggression significantly affect fan fanaticism. [ABSTRACT FROM AUTHOR]

This study aims to understand the motives for using Twitter @ohmybeautybank to meet the needs of beauty information by using the analysis of the Uses and Gratification theory introduced by Herbert Blumer and Elihu Katz in 1974. This theory is audience-centric because it is the audience that actively seeks and determines which media can meet their needs. The individual needs that the media satisfies according to Katz, Gurevitch, and Haas are cognitive, affective, personal integration, social integration, and tension release. The subjects in this study are late adolescents aged 18-22 years old students of Communication Sciences at Mulawarman University. The research method used is a descriptive qualitative method with data collection techniques through interviews, observations and documentation on three sources who are followers of @ohmybeautybank Twitter account. The results of the study showed that the motive for using @ohmybeautybank account was driven by cognitive, affective needs, personal integration, and tension release. Meanwhile, the need for social integration is not one of the main motives for users to strengthen relationships with others in carrying out their roles as social beings. [ABSTRACT FROM AUTHOR]

The popularity of camera phones, the availability of photo-editing apps, and the rise of visually oriented social media platforms have made it convenient for citizens to produce and circulate visual content in contentious politics. While scholars have increasingly recognized the role of visuals in mobilizing social-mediated protests, how different types of visuals affect message engagement across different stages of protests remains underexplored. For this study, we analyzed approximately ten million tweets from Twitter for three social-mediated protests (Black Lives Matter, Stop Asian Hate, and Women's March). We found that posts with images and videos generally attracted more audience engagement than their textual counterparts. Unpacking the role of visual media across different modalities and stages of social-mediated protests, we found that the superior effects of visuals were generally more pronounced during the ignition phase of the protest than the periods before and after. By applying unsupervised image clustering on millions of protest visuals, we systematically established four common visual content categories: crowd-based protest photos, non-crowd-protest human photos, non-human photos, and non-photograph visuals. We revealed heterogeneous effects on audience engagement across content categories and protests, and explored these categories through qualitative analysis of most-engaged visuals. These findings enrich our understanding of the mobilizing power of visual media in social movements and shed light on effective communication strategies regarding social inequalities. [ABSTRACT FROM AUTHOR]

Purpose: This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics. Design/methodology/approach: This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics. Findings: Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups. Practical implications: This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives. Social implications: This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces. Originality/value: This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context. [ABSTRACT FROM AUTHOR]

Conflicts over the independence of judiciary as one of the European Union's core democratic values is one indicator of democratic backsliding among its member states. Based on the Europeanization framework, we compare this conflict in German, Polish and Spanish print media and Twitter from 2019 to 2021. In the countries that are strongly affected by the value conflict, Poland and Spain, Europeanization is less evident. In contrast, the German discourse shows a high degree of Europeanization. We demonstrate that the print media is strongly elite-centric, while Twitter shows a higher actor visibility and inclusivity. However, we also identify important country differences. [ABSTRACT FROM AUTHOR]

Brexit was perceived as a Pandora's box moment by both Eurosceptic and pro-integration parties in the EU, as they expected it would embolden Euroscepticism by providing a paradigm to be followed. This article explores the initial reactions of nine Populist Radical Right parties to Brexit and how they evolved in tandem with the unfolding of negotiations. It also discusses possible reasons for the differentiation in the responses of those parties, from triumphant to moderated reactions. The empirical basis is a dataset that contains the public communications of these parties on Twitter between 2015 and 2020. The results show that although there was initial differentiation with some parties calling for referenda in their own countries, by 2017 every party's communication on Brexit drastically decreased, and by the time the UK left the EU (January 2020), calls for secession had disappeared from their discourses. [ABSTRACT FROM AUTHOR]

Free Open-Access Medical Education (FOAMed) has transformed medical education in the past decade by complementing and substituting for traditional medical education when needed. The attractiveness of FOAMed resources is due to their inexpensive nature, wide availability, and user ability to access on demand across a variety of devices, making it easy to create, share, and participate. The subject of nephrology is complex, fascinating, and challenging. Traditional didactic lectures can be passive and ineffective in uncovering these difficult concepts and may need frequent revisions. Active teaching methods like flipped classrooms have shown some benefits, and these benefits can only be multifold with current social media tools. Social media will inspire the involvement of students and allow them to create and share educational content in a "trendy way," encouraging the participation of their peers and thus building an educational environment more conducive to them while promoting revision and retainment. FOAMed also promotes asynchronous learning, spaced learning, microlearning, and multimodal presentation with a meaningful variation. This article discusses the evolution of digital education, social media platforms, tools for creating and developing FOAMed resources, and digital scholarship. [ABSTRACT FROM AUTHOR]

Oregon's Almeda Fire destroyed over 2,000 residences and displaced thousands of people who evacuated with little warning or guidance from official or traditional media sources. Results of this social network analysis of 12,193 tweets from the active stage of the crisis show a clear clustered network structure, with citizens playing a more influential role in crisis information diffusion than government or media accounts. Additionally, some of the most widely shared information from nonlocal media accounts was misinformed or inaccurate. Findings suggest that crisis leaders should work to cultivate relationships with influential citizen journalists in wildfire-prone areas. [ABSTRACT FROM AUTHOR]

Social media platforms offer people a variety of options to engage with politics, from directly following elected officials to discussing politics with social peers. Despite major advances in recent research on online political exposure through the lens of selective exposure, filter bubbles, and ideological echo chambers, little is known about the fundamental questions of what types of political actors people are exposed to on social media, and how these distinctive types vary across sociodemographic groups. We address this gap in the literature by analyzing unique panel data on more than 600,000 registered U.S. voters on Twitter during the 2020 U.S. Presidential campaign. We analyze this dataset to identify distinct types of political consumers and how they vary in terms of sociodemographics. Our findings suggest that the bulk of the population has a meaningful share of political content available from social peers, that the majority of this content originates from traditional sources of political information (media organizations, journalists, and politicians), and that media organizations are the dominant and direct source of political information on Twitter for nearly 20% of the sample population. These results advance our understanding of the way citizens learn about politics in new media and pave the way for next-step research to identify the causal effect of exposure to distinct curators of political content on individuals' political attitudes and political behavior. [ABSTRACT FROM AUTHOR]

Contrary to popular narratives, it is not clear whether using social media for news increases belief in political misinformation. Several of the most methodologically sound studies find small to nonexistent effects. However, extant research is limited by focusing on few platforms (usually Facebook, Twitter or YouTube) and is heavily U.S. centered. This leaves open the possibility that other platforms, such as those that rely on visual communication (e.g., Instagram) or are tailored to strong-tie network communication (e.g., WhatsApp), are more influential. Furthermore, the few studies conducted in other countries suggest that social media use increases political misperceptions. Still, these works use cross-sectional designs, which are ill suited to dealing with omitted variable bias and temporal ordering of processes. Using a two-wave survey fielded in Mexico during the 2021 midterm elections (N = 596), we estimate the relationship between frequency of news exposure on Facebook, Twitter, YouTube, Instagram and WhatsApp, and belief in political misinformation, while controlling for both time-invariant and time-dependent individual differences. In contrast to political discussion, information literacy and digital skills, none of the social platforms analyzed exhibits a significant association with misinformed beliefs. We also tested for possible indirect, moderated, and reciprocal relationships, but none of these analyses yielded a statistically significant result. We conclude that the study is consistent with the "minimal media effects" paradigm, which suggests that efforts to address misinformation need to go beyond social platforms. [ABSTRACT FROM AUTHOR]

This study examines the public discourse about vaccines on Twitter during the COVID-19 pandemic and compares it with the discourses about vaccines before the pandemic. Discourse, users’ profile, content, tone, vaccines, and sources of information were manually coded. We have identified a diversity of discourses related to vaccines. The discourse that has stood out the most during the pandemic is related to pandemic management measures. Nevertheless, both denialist and anti-denialist discourses are reduced compared to the previous period. Besides this, less polarization and more neutral discourse were identified during the pandemic. The two periods are expressly marked by tweets with content related to opinions and personal experiences regarding vaccines. Some of them are characterized, in general, by informality in how the users communicate their ideas. Interestingly, users from the medical-scientific sphere did not participate more in Twitter during the pandemic. There were fewer posts including scientific findings and more tweets about patient resources. On the other hand, the media and journalists were very active in this period by disseminating information and issuing vaccine opinions, reflecting their essential role in health crises. However, the presence of television channels, such as Fox News, can be an indication of conservative ideas on vaccines. Thus, the discourses have expressed different meanings in relation to the issue and disputed their legitimacy in this symbolic and virtual market. [ABSTRACT FROM AUTHOR]

Conferences are an integral part of academia, providing researchers with the opportunity to collaborate and disseminate their research. Traditionally these conferences have been in-person, but COVID-19 restrictions and growing recognition of systematic barriers have led many academic societies to reimagine the conference format. Social media platforms are often used to promote and broadcast in-person academic conferences, so with the development of online conferences they have naturally become hosting platforms. For example, during the peak of COVID-19 restrictions, when in-person meetings were not possible, Twitter conferences, like the first Global Animal Behaviour Conference (#AnimBehav2021), provided a much needed platform to collaborate and share science. Recently, conferences have returned to an in-person format. In addition, a change in ownership of Twitter led to widespread restructuring of the Twitter workforce and misinformation management policies that resulted in a massive exodus of academics from the platform. During these changes, we organized and held the second Global Animal Behaviour Twitter Conference (#AnimBehav2023) in January 2023. In this paper, we report (1) how engagement in the Twitter conference changed as a result of return to in-person conferences and changes in Twitter ownership, (2) how the community views online conferences in general and Twitter conferences in particular and (3) reflect on how online conferences can be organized moving forward. Given the widespread benefits of and need for online conferences, we urge academic societies and researchers to continue systematically exploring these alternative conference formats to ensure inclusivity and accessibility in academic spaces. • We evaluated the reach of the #AnimBehav2023 Twitter Conference. • We assessed engagement after in-person conferences returned and Twitter changed. • Presenting at a Twitter conference has greater reach than in-person conferences. • Global participation remained high despite alternative options and Twitter changes. • Despite continued enthusiasm, Twitter may no longer be the suitable platform. [ABSTRACT FROM AUTHOR]

Background: Analysis of X (formerly Twitter) posts can inform on the interest/perceptions that social media users have on health subjects. In this study, we aimed to analyse tweets on allergic conditions, comparing them with surveillance data. Methods: We retrieved tweets from England on "allergy," "asthma," and "allergic rhinitis," published between 2016 and 2021. We estimated the correlation between the frequency of tweets on "asthma" and "allergic rhinitis" and English surveillance data on the incidence of asthma and allergic rhinitis medical visits. We performed sentiment analysis, computing a score informing on the emotional tone of assessed tweets. We applied a topic modelling approach to identify topics (clusters of words frequently occurring together) for tweets on each assessed condition. Results: We analysed a total of 13,605 tweets on "allergy," 7767 tweets on "asthma," and 11,974 tweets on "allergic rhinitis." Food‐related words were preponderant on tweets on "allergy," while "eyes" was the most frequent meaningful word on "allergy rhinitis" tweets. We observed seasonal patterns for tweets on "allergic rhinitis," both in their frequency and sentiment – the incidence of allergic rhinitis medical visits was moderately to strongly correlated with the frequency (ρ = 0.866) and sentiment (ρ = −0.474) of tweets on "allergic rhinitis." For tweets on "asthma," no such patterns/correlations were observed. The average sentiment score was negative for all assessed conditions, ranging from −0.004 ("asthma") to −0.083 ("allergic rhinitis"). Conclusions: Tweets on "allergic rhinitis" displayed a seasonal pattern regarding their frequency and sentiment, which correlated with surveillance data. No such patterns were observed for "asthma." [ABSTRACT FROM AUTHOR]

This article provides a sociology of Twitter (now known as X) and charts the development of the study of the platform and its data in sociological venues through an analysis of 1,644 articles published since 2009. This review helps readers understand developments in the field and provides a road map for advancing future Twitter-related sociological research. The works cited in this review advance sociological research on a variety of subfields, including but not limited to race, social movements, segregation, politics, violence, and stratification. After a brief introduction of definitions as well as methodological approaches used to study the platform (computational, qualitative, and mixed), I explore how Twitter has been used in sociological research. Furthermore, using social movements and activism as a case study, I highlight what the platform's usage and communication reveal about the social world. I conclude with a road map for advancing Twitter-related sociological research in the current atmosphere in which Twitter has been renamed X. Ultimately, a sociology of Twitter does not need to be tethered to the platform per se, as it also provides a framework for understanding new platforms if they become home for what people previously posted to Twitter/X. [ABSTRACT FROM AUTHOR]

Purpose: In a dynamic, continuously evolving sports landscape, social media have become an indispensable tool for sports organizations to cultivate meaningful connections with fans. The rapid pace of technological advancements has elevated these digital platforms from a supplementary role to a pivotal position within strategic management frameworks. The existing literature explores how football clubs can utilize social media, but analyzing social media strategies within the context of football leagues is lacking. The absence of comparative studies benchmarking clubs across different geographical regions while simultaneously analyzing multiple social media platforms is especially noteworthy. In this study, a comprehensive analysis of social media engagement is undertaken within esteemed football leagues spanning Europe, South America and North America. Design/methodology/approach: Drawing on relationship marketing and employing content analysis as a methodological tool, the study examined 10,772 posts from the official accounts of eight football leagues on Facebook, Twitter and Instagram. Findings: Across the leagues, the findings reveal that content quality drives engagement more than frequency. In addition, several format combinations were identified that facilitate engagement and Instagram emerged as the top social media platform for generating fan engagement. Originality/value: This is one of the first empirical studies focusing on optimizing the use of social media to amplify fan engagement across various geographies and social media accounts and formats simultaneously. [ABSTRACT FROM AUTHOR]