Search Results (76)

This study employed Linguistic Inquiry and Word Count (LIWC-22) software, a language analysis tool, to examine Spanish-language pro-NSSI Tumblr posts. Pro-NSSI, or “pro non suicidal self-injury”, refers to online content that normalizes or supports self-harming behaviors. Given the strong associations between NSSI and conditions such as post-traumatic stress disorder (PTSD), anxiety, and depression, understanding how these behaviors are discussed online can help improve interventions. A year’s worth of public posts were collected, focusing on captions and hashtags that included NSSI-related terms. Using Linguistic Inquiry and Word Count (LIWC) software, we analyzed linguistic and psychological markers. Log-likelihood ratio tests revealed significantly higher frequencies of words related to negative emotions, sadness, health, and death compared to standard blog norms. Mixed-language posts showed notable code-switching, suggesting a possible emotional distancing mechanism when discussing self-harm. The findings indicate that Spanish-speaking adolescents engaging in pro-NSSI communities exhibit unique linguistic and psychological characteristics, with important implications for clinical assessment and intervention. Mental health counselors and educators can use these insights to develop culturally and linguistically responsive strategies for prevention and support. Full article

This study analyses social media networks in tourism using graphs focusing on user influence, hashtag patterns, and engagement. This study aims to reveal the structural function of core users, development of hashtags, and interaction patterns that construct tourism discourses. Using NodeXL 2024 for social network visualization and clustering analysis, this study measures centrality, modularity, and geodesic distances for influential user detection, topical dissemination, and engagement pattern identification. The results uncover bridging nodes between different communities, the proliferation of thematic hashtags related to sustainability and cultural heritage, and the role of emotional and visual storytelling in the use of engagement patterns. The theoretical implications also progress SNA application in tourism studies by illuminating aspects of how online discourses coalesce and the effect of SNA on access. In practical terms, this study indicates that destination marketers must consider leveraging key influencers, using strategic types of hashtags, and by monitoring engagement at key times to maximize effective destination marketing and to enhance crisis communication. These contributions notwithstanding, limitations involve the omission of sentiment analysis and the necessity for longitudinal data. By exploring new emerging platforms like TikTok and Instagram, researchers can begin to understand the more relevant trends of digital engagement. The present research offers a data-driven approach for facilitating the significance of integrating social media strategies with network externalities for tourism operators. Full article

We study the emergence of agency from scratch by using Large Language Model (LLM)-based agents. In previous studies of LLM-based agents, each agent’s characteristics, including personality and memory, have traditionally been predefined. We focused on how individuality, such as behavior, personality, and memory, can be differentiated from an undifferentiated state. The present LLM agents engage in cooperative communication within a group simulation, exchanging context-based messages in natural language. By analyzing this multi-agent simulation, we report valuable new insights into how social norms, cooperation, and personality traits can emerge spontaneously. This paper demonstrates that autonomously interacting LLM-powered agents generate hallucinations and hashtags to sustain communication, which, in turn, increases the diversity of words within their interactions. Each agent’s emotions shift through communication, and as they form communities, the personalities of the agents emerge and evolve accordingly. This computational modeling approach and its findings will provide a new method for analyzing collective artificial intelligence. Full article

Social media platforms play a significant role in facilitating business decision making, especially in the context of emerging technologies. Such platforms offer a rich source of data from a global audience, which can provide organisations with insights into market trends, consumer behaviour, and attitudes towards specific technologies, as well as monitoring competitor activity. In the context of social media, such insights are conceptualised as immediate and real-time behavioural responses measured by likes, comments, and shares. To monitor such metrics, social media platforms have introduced tools that allow users to analyse and track the performance of their posts and understand their audience. However, the existing tools often overlook the impact of contextual features such as sentiment, URL inclusion, and specific word use. This paper presents a data-driven framework to identify and quantify the influence of such features on the visibility and impact of technology-related tweets. The quantitative analysis from statistical modelling reveals that certain content-based features, like the number of words and pronouns used, positively correlate with the impressions of tweets, with increases of up to 2.8%. Conversely, features such as the excessive use of hashtags, verbs, and complex sentences were found to decrease impressions significantly, with a notable reduction of 8.6% associated with tweets containing numerous trailing characters. Moreover, the study shows that tweets expressing negative sentiments tend to be more impressionable, likely due to a negativity bias that elicits stronger emotional responses and drives higher engagement and virality. Additionally, the sentiment associated with specific technologies also played a crucial role; positive sentiments linked to beneficial technologies like data science or machine learning significantly boosted impressions, while similar sentiments towards negatively viewed technologies like cyber threats reduced them. The inclusion of URLs in tweets also had a mixed impact on impressions—enhancing engagement for general technology topics, but reducing it for sensitive subjects due to potential concerns over link safety. These findings underscore the importance of a strategic approach to social media content creation, emphasising the need for businesses to align their communication strategies, such as responding to shifts in user behaviours, new demands, and emerging uncertainties, with dynamic user engagement patterns. Full article

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. Full article

The era of digital capitalism poses conundrums for communication for development and social change scholarship and practice. On one hand, mainstream social media platforms are an increasingly ubiquitous element of the everyday media practices of growing portions of the global population. On the other, the profit-driven architectures can make these hostile spaces for progressive social change dialogues. While a burgeoning literature exists on the uses of social media as part of hashtag-activism and social movements, much less critical consideration has been given to NGOs’ and civil society organizations’ uses of capitalist-driven social media platforms in their development and social change efforts, and the challenges and compromises they navigate in this, consciously or not. This paper argues that meaningful uses of social media platforms for social change requires cultivating a hacker mindset in order to find tactics to subvert, resist, and appropriate platform logics, combined with an ecological sensibility to understanding media and communication. This paper analyzes how metaphors, specifically of a recipe, can offer a productive, praxis-oriented framework for fostering these sensibilities. The paper draws on insights from workshops with IT for Change, a civil society organization in India, which is both a leader in critiquing the political and economic power of Big Tech especially in the Global South, and beginning to use Instagram for its work on adolescent empowerment. Full article

Social media platforms, such as Instagram, provide space for marginalized groups to connect, learn about and express themselves, and cultivate community. Trans Latinas, a group target of violence and discrimination, resist by expressing themselves and building community through social media. As cisgender researchers, we explored how trans Latinas use #translatina on Instagram as a shared space to present themselves and their identities, to leverage this knowledge in our fields. We analyzed 134 posts in February and March of 2020 employing basic and interpretive content analyses while considering Goffman’s theory of presentation of self. Results showed that trans Latinas mostly presented individually through posed selfies taken near the camera, using a straight camera angle, standing, not smiling, and making eye contact. Most users wore makeup, styled hair, and accessories. Analyzing written captions and photos, four themes were constructed to understand how trans Latinas presented their identities and connected with others: (1) expressions of beauty and femininity, (2) fostering community, (3) commercial or work, and (4) feeling good and confident. These results have implications for mental health and health promotion practices, as social media could serve as affirming spaces for trans Latinas to reinforce their self-determination, maintain a sense of self, and build community. Full article

Women are underrepresented in many areas of journalistic newsrooms. In this paper, we examine if this established effect persists in the new forms of journalistic communication, namely social media networks. We use mentions, retweets, and hashtags as measures of journalistic amplification and legitimation. Furthermore, we compare two groups of journalists in different stages of development: political and data journalists in Germany in 2021. Our results show that journalists identified as women tend to favor other women journalists in mentions and retweets on Twitter (now called X), compared to men. While both professions are dominated by men, with a high share of tweets authored by men, women mention and retweet other women more than their male colleagues. Female data journalists also leverage different sources than men. In addition, we found data journalists to be more inclusive of non-member sources in their networks compared to political journalists. Full article

Social media platforms allow people to share information, connect, and build networks at an unprecedented scale with positive and negative consequences. Social network analysis (SNA) applies mathematical network and graph theory to visualise information transfer as relational networks of connected nodes. Measuring node connectivity (centrality) permits the identification of ‘influencers’. SNA has been applied to analyse the spread of misinformation on Twitter (1), but to date, no research has examined nutrition networks. Therefore, this study examined the #Nutrition conversations on Twitter utilising SNA and linguistic analyses. English language tweets including ‘#Nutrition’ on 1–21 March 2023 were collected using the SNA tool, NodeXL Pro (Network Overview for Discovery and Exploration in Excel) (2). SNA is a multistep process that calculates graph metrics and develops a network graph to measure the relationships between users. SNA also identifies semantically related words, hashtags, and word pairs and identifies the sentiment of words used, as measured against the Opinion Lexicon (2). The #Nutrition network included 17,129 vertices (users) with 26,809 unique edges (connections); edges with duplicates were merged. The network density was low, suggesting that most users communicate heavily with a small number of users. The average geodesic distance between any two users was 5.26, revealing a dispersed online discussion. SNA identified the top 10 influencers in this network, measured by high betweenness centrality (23,375,543–5,207,998). Influential users were from a mix of accounts including personal, online blogs, and government organisations. High betweenness centrality identified the users with the greatest influence, acting as bridges between network groups and therefore amplifying #Nutrition messages. Sentiment analysis found the discourse was more positive (0.047, 22,218 words) than negative (0.015, 6795 words). Semantic analysis calculated the total words, 468,191, and identified the most frequently used words in the tweets: #nutrition, #health, food, more, nutrition, health, #diet, #healthylifestlye, #fitness, and #food. Social network analysis shows the discourse on Twitter relating to #Nutrition is dispersed without clear polarising views. Semantic analysis showed that ‘health’ was the main topic discussed in relation to nutrition in this network and was most frequently associated with #Nutrition. The narrative was positively framed, as identified through sentiment analysis. Full article

In 2019, a noise complaint from a new, white resident of Shaw, a historically Black neighborhood of Washington, DC, led a local MetroPCS store to mute the go-go music that the storefront had played on its outdoor speakers for decades. The cultural and social implications of muting go-go music, a DC-originated genre of music that has played a central role in DC Black culture, inspired a viral hashtag, #dontmutedc, on social media, as well as a series of high-profile public protests against the muting. The #dontmutedc protests highlighted the increasing impact of gentrification on DC’s Black communities, and connected gentrification to several other important social issues affecting Black DC residents. In the wake of the #dontmutedc incident, several DC-area activist organizations have integrated go-go music into major, public-facing racial justice projects. The first part of this article presents a brief history of go-go music and race in DC community life, mainstream media, and law enforcement in order to contextualize the work of go-go-centered activist work in the aftermath of the #dontmutedc protests. The second part of this article highlights the go-go-centered activist work of three organizations: the Don’t Mute DC movement, Long Live Go-Go, and the Go-Go Museum and Café. These movements’ projects will be used to categorize three distinct approaches to go-go-centered racial justice activism in the Washington, DC, area. Full article

Social media platforms such as Twitter/X are increasingly important for political communication but the empirical question as to whether such communication enhances democratic consensus building (the ideal of deliberative democracy) or instead contributes to societal polarisation via fostering of hate speech and “information disorders” such as echo chambers is worth exploring. Political deliberation involves reciprocal communication between users, but much of the recent research into politics on social media has focused on one-to-many communication, in particular the sharing and diffusion of information on Twitter via retweets. This paper presents a new approach to studying reciprocal political communication on Twitter, with a focus on extending network-analytic indicators of deliberation. We use the Twitter v2 API to collect a new dataset (#debatenight2020) of reciprocal communication on Twitter during the first debate of the 2020 US presidential election and show that a hashtag-based collection alone would have collected only 1% of the debate-related communication. Previous work into using social network analysis to measure deliberation has involved using discussion tree networks to quantify the extent of argumentation (maximum depth) and representation (maximum width); we extend these measures by explicitly incorporating reciprocal communication (via triad census) and the political partisanship of users (inferred via usage of partisan hashtags). Using these methods, we find evidence for reciprocal communication among partisan actors, but also point to a need for further research to understand what forms this communication takes. Full article

This study examines how U.S. senators strategically used hashtags to create political communities on Twitter during the 2022 Midterm Elections. We propose a way to model topic-based implicit interactions among Twitter users and introduce the concept of Building Political Hashtag Communities (BPHC). Using multiplex network analysis, we provide a comprehensive view of elites’ behavior. Through AI-driven topic modeling on real-world data, we observe that, at a general level, Democrats heavily rely on BPHC. Yet, when disaggregating the network across layers, this trend does not uniformly persist. Specifically, while Republicans engage more intensively in BPHC discussions related to immigration, Democrats heavily rely on BPHC in topics related to identity and women. However, only a select group of Democratic actors engage in BPHC for topics on labor and the environment—domains where Republicans scarcely, if at all, participate in BPHC efforts. This research contributes to the understanding of digital political communication, offering new insights into echo chamber dynamics and the role of politicians in polarization. Full article

Zero waste (ZW), the concept of reducing waste production, is now becoming a lifestyle trend. Social media is strengthening this by popularizing the movement and connecting related communities. ZW and food are closely related, since food waste is a significant sustainability issue; however, the exact relationship between ZW and food communication on social networks is not clear. This study analyzed user communication on the social networking site Twitter between July 2008 and April 2023 to determine how members communicated and shared topics related to ZW and food; an analysis of hashtag frequency was also conducted. During the study period, a total of 50,650 tweets with both #zerowaste and #food hashtags were recorded, written by 21,271 unique users from all over the world. Topic analysis identified the nine related topics: ZW lifestyle, leftover recipes, ZW events, food rescue, climate change, packaging, ZW stores, composting, and ZW restaurants; visual analysis indicated that these topics were closely connected, suggesting common membership of these communities. Overall, our results provide insight into the ZW and food communities on Twitter, which may be useful for marketers, influencers, and government agencies to create targeted content and facilitate wider adoption of a ZW lifestyle. Full article

Sociolinguistic styles and the resultant ascribed identities are understood as the product of simultaneous variables, leading to the notion of bricolage, or the co-occurrence of variables and their collective indexical meanings. Relatively little attention has been paid to these processes as they manifest on social media platforms. The goal of the current paper is to understand which linguistic and thematic features co-occur in the online production of the no sabo kid style and identity, which manifests as a form of linguistic discrimination towards U.S. Latinx youth. “Hashtag communities” were used to locate posts about no sabo kids on TikTok (N = 95), and videos were automatically and manually coded for salient linguistic and discursive resources in the online no sabo kid community. The results show the co-occurrence of code-switching and phonological and lexical variation, alongside discursive themes, namely ‘proficiency’, ‘ethnicity’, and ‘performative lexical gaps’. I argue that the no sabo kid hashtag community is a mediated manifestation of ideologies surrounding U.S. Latinx bilinguals, where a supposed lack of proficiency in Spanish and grammatical blending of Spanish and English index inauthentic ethnicity. Mediated instantiations of sociolinguistic styles shed light on how linguistic features become enregistered through multimodality and semiotic bricolage. Full article

The recent rates of global change in nature are unprecedented in human history. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) has proposed a framework to achieve transformative change. Transformative change with respect to nature will be driven by recognizing the values people have; making inclusive decisions based on these values; restructuring policies, rights, and regulations in accordance with them; and transforming social norms and goals that can drive change. Social media is a new source of information and a modern tool for monitoring public opinion on human–nature interactions. This study identified commonalities among seven language communities (the six official languages of the United Nations and the Japanese language), demonstrating the uniqueness of the Japanese community by comparing hashtags in tweets that include the term biodiversity and determining differences in interest and concern about biodiversity from the past to the present. Tweets accessible at the end of 2021 that focus on biodiversity were collected from the Twitter server and used to form a text dataset. Interest was then qualitatively and quantitatively identified using natural language processing technology. Engagements and diversity indices were found to be on the rise in all language communities. We found that the Japanese language community has a different perspective on the relationship between biodiversity and humans from the scope of the IPBES conceptual framework. Future work should examine the relationship between passion for biodiversity and the Sustainable Development Goals. In addition, collaboration with various people around the world is necessary to understand the concept of biodiversity in different traditions and cultures. Full article