Tweeting about sexism: The well-being benefits of a social media collective action.

Although collective action has psychological benefits in non-gendered contexts (e.g., Drury et al., 2005), the benefits for women taking action against gender discrimination are unclear. This study examined how a popular, yet unexplored potential form of collective action, namely tweeting about sexism, affects women’s well-being. Women read about sexism and were randomly assigned to tweet, or to one of three control groups. Content analyses showed tweets exhibited collective intent and action. Analyses of linguistic markers suggested public tweeters used more cognitive complexity in their language than private tweeters. Profile analyses showed that compared to controls, only public tweeters showed decreasing negative affect and increasing psychological well-being, suggesting tweeting about sexism may serve as a collective action that can enhance women’s well-being

Mapping networks of influence: tracking Twitter conversations through time and space

The increasing use of social media around global news events, such as the London Olympics in 2012, raises questions for international broadcasters about how to engage with users via social media in order to best achieve their individual missions. Twitter is a highly diverse social network whose conversations are multi-directional involving individual users, political and cultural actors, athletes and a range of media professionals. In so doing, users form networks of influence via their interactions affecting the ways that information is shared about specific global events. This article attempts to understand how networks of influence are formed among Twitter users, and the relative influence of global news media organisations and information providers in the Twittersphere during such global news events. We build an analysis around a set of tweets collected during the 2012 London Olympics. To understand how different users influence the conversations across Twitter, we compare three types of accounts: those belonging to a number of well-known athletes, those belonging to some well-known commentators employed by the BBC, and a number of corporate accounts belonging to the BBC World Service and the official London Twitter account. We look at the data from two perspectives. First, to understand the structure of the social groupings formed among Twitter users, we use a network analysis to model social groupings in the Twittersphere across time and space. Second, to assess the influence of individual tweets, we investigate the ageing factor of tweets, which measures how long users continue to interact with a particular tweet after it is originally posted. We consider what the profile of particular tweets from corporate and athletes’ accounts can tell us about how networks of influence are forged and maintained. We use these analyses to answer the questions: How do different types of accounts help shape the social networks? and, What determines the level and type of influence of a particular account

False News On Social Media: A Data-Driven Survey

In the past few years, the research community has dedicated growing interest to the issue of false news circulating on social networks. The widespread attention on detecting and characterizing false news has been motivated by considerable backlashes of this threat against the real world. As a matter of fact, social media platforms exhibit peculiar characteristics, with respect to traditional news outlets, which have been particularly favorable to the proliferation of deceptive information. They also present unique challenges for all kind of potential interventions on the subject. As this issue becomes of global concern, it is also gaining more attention in academia. The aim of this survey is to offer a comprehensive study on the recent advances in terms of detection, characterization and mitigation of false news that propagate on social media, as well as the challenges and the open questions that await future research on the field. We use a data-driven approach, focusing on a classification of the features that are used in each study to characterize false information and on the datasets used for instructing classification methods. At the end of the survey, we highlight emerging approaches that look most promising for addressing false news

The leading women: The media representation of minor party leadership during the 2019 UK General Election

This thesis analyses the media representation of Nicola Sturgeon, Jo Swinson, and Arlene Foster during the 2019 UK General Election. Each politician within this thesis was serving in a different political system enabling an expansive look at the representation of political leadership across the UK. I examined newspapers, Twitter, and the televised election debates to review the self-presentation and representation of these three female politicians in mediated spaces. Amongst my selection of media, I reviewed the personal Twitter accounts of these political women, their media representation in London-based, Scottish, and Northern Irish national publications, and the televised debates broadcasted on the BBC, ITV, STV, and Channel 4. I analysed these three forms of media by using a mixed methods approach combining content analysis and critical discourse analysis. In this study, there were interesting differences between the three women in terms of explicitly gendered coverage: Foster's coverage was the least gendered, Swinson's the most. At the same time, the media representation of Sturgeon included gendered commentary that was positive in tone and used to present her as politically accomplished. The second prominent finding of the study was the variation in coverage between London-based, Scottish, and Northern Irish newspapers for each politician’s media representation. In addition, each party leader held political positions in different countries of the UK, revealing expressions of banal nationalism within their self-presentation. Notably, the reference to national belonging was most impactful in my Foster and Northern Irish datasets. Foster's selfpresentation and media representation were often focused on Northern Irish issues rather than the whole of the UK. Foster's prominence as a local figure in Northern Ireland and her constituency showed that various methods of analysis are needed to study politicians less prominent in a UK-wide election compared to political figures like Sturgeon and Swinson, who became a part of the national media agenda.This thesis analyses the media representation of Nicola Sturgeon, Jo Swinson, and Arlene Foster during the 2019 UK General Election. Each politician within this thesis was serving in a different political system enabling an expansive look at the representation of political leadership across the UK. I examined newspapers, Twitter, and the televised election debates to review the self-presentation and representation of these three female politicians in mediated spaces. Amongst my selection of media, I reviewed the personal Twitter accounts of these political women, their media representation in London-based, Scottish, and Northern Irish national publications, and the televised debates broadcasted on the BBC, ITV, STV, and Channel 4. I analysed these three forms of media by using a mixed methods approach combining content analysis and critical discourse analysis. In this study, there were interesting differences between the three women in terms of explicitly gendered coverage: Foster's coverage was the least gendered, Swinson's the most. At the same time, the media representation of Sturgeon included gendered commentary that was positive in tone and used to present her as politically accomplished. The second prominent finding of the study was the variation in coverage between London-based, Scottish, and Northern Irish newspapers for each politician’s media representation. In addition, each party leader held political positions in different countries of the UK, revealing expressions of banal nationalism within their self-presentation. Notably, the reference to national belonging was most impactful in my Foster and Northern Irish datasets. Foster's selfpresentation and media representation were often focused on Northern Irish issues rather than the whole of the UK. Foster's prominence as a local figure in Northern Ireland and her constituency showed that various methods of analysis are needed to study politicians less prominent in a UK-wide election compared to political figures like Sturgeon and Swinson, who became a part of the national media agenda

The Botization of Science? Large-scale study of the presence and impact of Twitter bots in science dissemination

Twitter bots are a controversial element of the platform, and their negative impact is well known. In the field of scientific communication, they have been perceived in a more positive light, and the accounts that serve as feeds alerting about scientific publications are quite common. However, despite being aware of the presence of bots in the dissemination of science, no large-scale estimations have been made nor has it been evaluated if they can truly interfere with altmetrics. Analyzing a dataset of 3,744,231 papers published between 2017 and 2021 and their associated 51,230,936 Twitter mentions, our goal was to determine the volume of publications mentioned by bots and whether they skew altmetrics indicators. Using the BotometerLite API, we categorized Twitter accounts based on their likelihood of being bots. The results showed that 11,073 accounts (0.23% of total users) exhibited automated behavior, contributing to 4.72% of all mentions. A significant bias was observed in the activity of bots. Their presence was particularly pronounced in disciplines such as Mathematics, Physics, and Space Sciences, with some specialties even exceeding 70% of the tweets. However, these are extreme cases, and the impact of this activity on altmetrics varies by speciality, with minimal influence in Arts & Humanities and Social Sciences. This research emphasizes the importance of distinguishing between specialties and disciplines when using Twitter as an altmetric

‘I’m Not a Virus’: Asian Hate in Donald Trump’s Rhetoric

Since the start of Covid-19, anti-Asian sentiment spiked. From March 2020 to June 2021, there were a total of 9,081 self-reported incidents of hate across the United States (Stop AAPI Hate. (2021). As Covid-19 spread into the U.S., President Trump immediately blamed China by referring to the virus as the ‘Chinese Virus’ and used the hashtag #ChineseVirus on Twitter (Weise, E. 2021). Anti-Asian hashtags soared after Donald Trump first tied COVID-19 to China on Twitter. (USA Today. https://www. usatoday.com). Anti-Asian rhetoric expressed on Twitter grew after Trump’s tweet about the ‘Chinese virus,’ and the number of Chinese and other Asian hate crimes grew exponentially. This study explores the rhetorical strategies that Trump utilized to create a sense of fear against the dangerous ‘Other.’ We use a rhetorical thematic analysis to analyze Trump’s tweets that contain language such as ‘Chinese virus’ or ‘Kung Flu.’ Themes such as scapegoating, fear of the other, China bashing, and populist appeals were prevalent. Describing Chinese and other Asian bodies as ‘spreaders’ of diseases, reinforces the Yellow Peril and perpetual foreigner stereotypes. The study shows the importance of presidential rhetoric in influencing public opinion in the context of COVID-19 and Asian hate

Investigating How School Counselors Using #scchat on Twitter Advocate for Marginalized Student Populations: A Social Network Analysis

Advocacy work for marginalized student populations can help eliminate systemic barriers in schools. School counselors interact with millions of P-12 students daily and have the unique skills to advocate for marginalized students (U.S. Department of Education, 2019; Edirmanasinghe et al., 2022). Social media platforms such as Twitter allow school counselors to advocate outside of the school setting. It also allows them to obtain professional development and connect to other professionals (Schultz, 2022). In this study, the researcher seeks to learn how school counseling professionals\u27 use of Twitter changed over ten years when using the #scchat hashtag and how their advocacy for marginalized student populations on Twitter has changed over time. Twitter Application Programming Interface (API) data was collected via NodeXL over a ten-year period, allowing the researcher to identify sample years of network data to answer the research questions. The researcher used Social Network Analysis (SNA) to identify relationships and influencers within the networks and quantitative content analysis to analyze the frequency of hashtags, specific words, and sentiment. The results show significant growth in using #scchat regarding the number of tweets and users. The density of the graphs decreased as the networks became more extensive, and the number of group clusters increased. The results also show increased advocacy over the ten years using words such as advocacy, advocate, equity, race, ethnicity, racism, transgender, LGBTQIA+, Black, Latinx, Asian, and Native American

Online hate speech and emotions on Twitter: a case study of Greta Thunberg at the UN Climate Change Conference COP25 in 2019

The presence of environmental activist Greta Thunberg at the UN Climate Change Conference COP25 in 2019 prompted reactions on social media, which grew exponentially after she was named Time Magazine's Person of the Year 2019 and even more so after then-president of the United States Donald Trump tweeted his reaction to her accolade. An analysis of 1,395,054 tweets gathered between November and December 2019 through R, network theory techniques, machine learning and natural language processing showed how messages sparking hatred and intense emotions generate posts, mainly negative ones that subsequently serve as catalysts. The results also demonstrate the relevance of the bubble filter and echo chamber theories and the fact that hate springs from a range of sentiments depending on each participant group

Sharing economy: exploring social media and bibliometric evidence

The current structural changes in the world economy have led to the emergence and rapid proliferation of a new economic model whose individuals share assets owned by others. Thus was born the concept of Sharing Economy. This concept has been applied in several sectors with success as it is the case of the transport sector and the real estate sector. However, the Sharing Economy has become a complex phenomenon with several ramifications in different aspects, two of these same slopes are: academic and social. This thesis will focus on a big-time analysis of both strands. On the academic side, supporting a bibliometrics analysis will try to understand what the repercussions of this phenomenon at the level of academic publications, analyzing number of articles per year, authors, publications, terms and key articles. On the social side, relieving Crimson Hexagon's ForSight analysis software will analyze, number of tweets per year, authors and important events on the social network Twitter. This analysis has three main aims: firstly, to understand the phenomenon of Economy Sharing in the two aspects studied, secondly, to perceive the differences existing in the in the two strands studied and finally, using altmetrics discovering what is the difference between social relevant articles and the academic relevant articles of Sharing Economy.As atuais alterações estruturais na economia mundial levaram ao aparecimento e rápida proliferação de um novo modelo económico cujos indivíduos partilham ativos detidos por outros. Assim nasceu o conceito de Sharing Economy. Este conceito foi aplicado em diversos setores com sucesso como é o caso do setor dos transportes e do setor imobiliário. Contudo, a Sharing Economy tornou-se um fenómeno complexo com diversas ramificações em diferentes vertentes, duas dessas mesmas vertentes são: a académica e a social. Esta tese centrar-se-á numa análise de big data de ambas as vertentes. Na vertente académica, suportando de uma análise de bibliometria irá tentar perceber qual as repercussões deste fenómeno ao nível de publicações académicas, analisando número de artigos por ano, autores publicações, expressões e artigos chave. Na vertente social, socorrendo software de análise ForSight da Crimson Hexagon irá se preceder uma análise temporal do número de tweets por ano, autores e eventos importantes. Esta análise tem três intuitos principais: em primeiro lugar compreender o fenómeno do Sharing Economy nas duas vertentes estudadas, em segundo lugar, perceber as diferenças existentes nas duas vertentes estudadas e, por fim, e usando altmetrics, descobrir as diferenças entre artigos socialmente relevantes e academicamente relevantes sobre Sharing Economy