Twitter Space Research Articles

Cyberfeminist resistance against hegemonic and anti-feminist discourses: the case of Kuwait

ABSTRACT How do Kuwaiti cyberfeminists resist hegemonic and anti-feminist discourses? This article seeks to answer this question by exploring the contestation between Kuwaiti feminists and their Islamist opponents on Twitter. Viewing cyberfeminism as constructive resistance, I argue that feminism is practiced and performed online by a variety of women actors who do not necessarily self-identify as feminists or activists, constructively and continuously producing new discourses and subjectivities, establishing a salient embodied feminist presence that can trigger an anti-feminist counter-mobilization. In a Twitter Space hosted by the Kuwaiti Ministry of Awqaf and Islamic Affairs on December 23, 2021, an anti-feminist lecture portrayed Kuwaiti feminists as alienated from religion and society while downplaying the extent of violence against women in the country. In response, Kuwaiti cyberfeminists attempted to reclaim feminism by placing it within local and national frames using a Twitter hashtag. Resorting to a reverse discourse strategy had some shortcomings, including reinforcing hegemonic discourses and the binary of “us” (feminists) versus “them” (Islamists), which may have been an inevitable outcome of an antagonistic narrative. This article adds to the existing literature on cyberfeminism by exploring an understudied domain and offering significant insights into feminists’ online resistance in socially conservative contexts.

Evaluating the effectiveness of social media in health communication: assessing the impact and outcomes of health communication campaigns conducted on x (Twitter)

Twitter, a leading health communication channel, has effectively promoted health awareness and continues to do so. It provides health information quickly and easily, serving as a vital source of health information. Health practitioners often use Twitter to disseminate health information, a trend that has become more prevalent than engaging audiences in multi-way conversations and interactions. This reassures us of the platform's effectiveness in reaching a broad audience. Twitter has emerged as a powerful tool for reaching a broader audience, offering platforms like Twitter Space for discussion and engagement with health professionals. This technology has brought a new dimension to healthcare, enlightening the public and promoting health behaviors, benefits, challenges, and preventive measures. This research delved into the effectiveness of Twitter in health communication. The study employed a quantitative approach, using a survey to assess the impact of health-related content on Twitter and evaluate the effectiveness of shared health communication. The anticipated outcomes of this research include a comprehensive understanding of the effectiveness of Twitter on health communication, insights into the effectiveness of health content Twitter users access, and recommendations for improving health outcomes through targeted audience campaigns by providing empirical evidence on the effectiveness of Twitter on health communication and a positive and crucial role play in promoting health awareness. Twitter has been used as a tool for health communication campaigns, and many Twitter users benefit from the health information gained there

The Making of #CovidTwitter: Who Were the Loudest “Covid Influencers” and What Did They Say About the COVID-19 Pandemic?

This study explores COVID-19 communications disseminated by the top 100 most followed Twitter profiles—what we call the Twitter influencing elite. Focusing on a critical period from January to July 2020, we conducted a quantitative and qualitative analysis of 6,602 tweets about COVID-19 produced by these Covid Influencers. The findings reveal that approximately two-thirds of the COVID-19 tweets in our sample originated from established global media organizations. While these sources were prominent, they were not the “loudest” in terms of engagement and virality. That belonged to powerful politicians like Trump and Obama, popular singers such as Harry Styles and Taylor Swift, and business personalities like Elon Musk. What is more, our qualitative analysis highlights how the affordances of the digital space and the context of the pandemic were leveraged by these influential Twitter users to advance their own agendas. For instance, some blended health information and caring narratives with promotional appeals, while others, such as Elon Musk and Donald Trump, engaged in political agitation and/or anti-care discourses creating a staccato of conflicting messaging and mis/dis-information. These findings demonstrate that the Twitter space is as political and politicized as it is commercial and commercialized. We conclude that digital influencers and celebrities cannot just simply be used to produce communications during times of crisis as many across the study of health and medical communication have argued. The involvement by digital influencers and celebrities—much like the Covid Influencers we examined here—in spreading information must be critically scrutinized, considering the potential for mixed motives, agendas, and real-world outcomes.

Review Paper: Social Media Sentiment Analysis Using Twitter Dataset

Review Paper: Social Media Sentiment Analysis Using Twitter Dataset

Politics of meaning about quality of education in Ethiopia

Background: In recent years, the policymakers in Ethiopia are engaged in reforming the curriculum to address the poor quality of education in the country. The public as well as the different stakeholders have been engaged in debating on the topic. The discourse about the education policy has been intense at times.Aim: The purpose of this study is to provide a framework for the different meanings of quality of education in Ethiopia that arise from different experiences and factors among the participants. This, in turn, can guide and shape the intense discourse among the different stakeholders.Setting: The Ethiopian diaspora group arranged a two-part Twitter Space discussion among the Ethiopian diasporas who are engaged in the affairs of their home country to discuss the education policy of the country. The theoretical approach about politics of meaning by Ole Skovsmose was used to reflect on the discussion made by this community.Methods: Discourse analysis has been used on audio data from the Twitter Space conversation to trace different meanings of quality of education that different stakeholders attribute within the context of Ethiopia.Results: Seven different politics of meanings about quality of education has been identified: the good old days, decolonise, expectation, moral, foundation, opportunity and vision meaning.Conclusion: The authors believe that these seven meanings can provide insight to the policymakers in their endeavour to improve quality. In fact, these meanings are generic to any education system, and this can be investigated further.Contribution: Even though the authors’ analysis depended on the information obtained from the Ethiopian diaspora, the findings can be generalised to the wider Ethiopian public, the origin of the informants.

Time for different stories: Reflections on IPONS panel addressing current debates in nursing theory, educationand practice.

On 4 February 2021 a group of nurse scholar-educators, nurses and other interested folks came together for the second of two virtual events to think together about the role of philosophy in the nursing world. The live streamed open access event provided an opportunity in the COVID-19 pandemic for over 400 people to listen to five nursing scholars' presentations and to interact virtually through comments in chat and on the @IPONSociety Twitter social media platform. By reading the comments and questions that were generated, and by looking at the social media comments related to the event, it is apparent that philosophy is an important thinking practice for nurses but many audience members critically expressed they felt excluded. Critical issues were raised by participants in chat and on Twitter-pointedly around the need for more representative voices-including the imperative to open nursing philosophy to diverse and disparate worldviews. This dialogue provides a summary of critical points raised during the live question and answer session for the panel entitled Addressing Current Debates in Nursing Theory, Education, Practice as well as examing comments selected from the @IPONSociety Twitter space in response to the panel. One commenter said it was great to see the discussion being lifted up from the influential roots of white supremacy, while other nurses expressed that they wished the panellists themselves were more diverse. In discussion of key takeaway, links are made to historical and ongoing structural oppressions in nursing where thinking practices like nursing philosophy and theory are still dominated by world views emanating from positionalities of able-bodied cis-gendered heterosexual western eurocentric whiteness.

Twitter as a Tool for Youth Mobilisation in Nigeria: A Study of the EndSARS Protest in Nigeria

This study examines how Twitter aided the youth mobilisation for the EndSARS protest and if the Celebrities' tweets on Twitter instigated participation by the youth. An online questionnaire-based cross-sectional study design was adopted for this research. The information collected with the questionnaire includes the sociodemographic parameters, information on the role of social media in their participation in the end SARS protest, and the contribution of celebrity tweets. A total of 308 respondents aged between 20 – 39 years were interviewed by the end of the study, and their descriptive analysis was presented using SPSS version 25. The result showed that most of the participants in the EndSARS protest were moved by the information gotten from the Twitter space 231(75.0) and that posts on Twitter influence the decision-making processes of many of the respondents but to different degrees. Unexpectedly, though a significant number 154(50.0) of the respondents followed celebrity tweets during the protest, many 182(59.1) claimed those tweets had no additional influence in encouraging them to join the protest as they would have still participated in the protest with or without them.

Transcript of: Active Inference Twitter Space #001 "What is Buddha Mind? How does Active Inference relate?"

Transcript of: Active Inference Twitter Space #001 "What is Buddha Mind? How does Active Inference relate?"

Hatenography On Twitter During the Covid-19 Pandemic in Indonesia: Hate Speech Case Against Anies Baswedan

The Covid-19 pandemic's handling was not merely a health problem but created a discourse battle in the social media space. The policies decided by the central and local governments often provoke public reactions in the form of criticism to hate speech. DKI Jakarta Governor Anies Baswedan has one of the governments figures whose policies have often been attacked by criticism and hate speech. This study aimed at analyzing the trend of Anies Baswedan's phrases. The phrases related to the handling of COVID-19 in netizen conversations in the Twitter space and collecting patterns of hate speech about Anies Baswedan in pro-government and contra-government accounts connected with the policies of handling COVID-19. This study used a virtual ethnography method against hate speech involving written verbal messages and communication objectives from Twitter users (influencers) who commented on Anies Baswedan's policies in dealing with Covid-19. The captured data come from two groups of accounts, namely government supporter and opposition. Data exploration used https://exportcomments.com/ software for comments, while trend data and public psychology (sentiment and perception) used Intelligence Socio-analytics (ISA) from March-August 2020. Data were taken from 42,393 total replies and 1,010,007 total posts for ISA. Qualitative data processing used Nvivo 12 plus to arrange phrases and speech patterns. The result showed that Anies Baswedan associated with COVID-19 had significantly increased since march 2020 and decreased in the following months. The tweets about Anies Baswedan had built public psychology in anticipatory emotions and anger, neutral and positive sentiments. The majority public's perception of Anies Baswedan is disgusting. Pro-government accounts had made Anies Baswedan the theme of conversation in their tweets. The narrative that was built was to criticize and or belittle the governor's policies and actions. Tweets with harmful contents (insulting, condescending, disgusting, swearing, and accusations) were the dominant model, triggering replies in the same pattern, but with more crude, ironic, innocent satirical content using nouns or animal names and other, more sarcastic terms. © 2021. RIGEO. All Rights Reserved.

Twitter as a clique: journalists' patterns of Twitter use in Israel

PurposeApproximately 75% of journalists in Western society maintain a Twitter account. This significant presence underscores Twitter's impact on the public agenda. The purpose of this paper is to gain an understanding of journalists' practices on Twitter, from the perspective of journalists in Israel, and explore whether journalists use Twitter's potential as a pluralistic platform of expression.Design/methodology/approachThe authors conducted in-depth interviews with leading journalists working on traditional media and those active on Twitter in Israel.FindingsAlthough Twitter is an open, inclusive platform for opinion exchange, in which journalists have a sense of unfettered freedom of expression, findings indicate that in Israel, Twitter effectively functions as an echo chamber used by a clique of journalists and politicians in a manner than reifies the hegemonic system. The majority of the public do not use Twitter to draw attention to contentious issues, and journalists almost exclusively continue to set and control the agenda on this platform. As a result, journalists become even more disconnected from society. Therefore, Twitter has been found to be a complementary platform to traditional media that helps to preserve the existing social order.Social implicationsThis paper offers not only a theoretical contribution but also social relevance, as the topics and issues discussed in the paper might concern citizens and policymakers. This paper contributes to the debate on freedom of the press, which is an important principle of pluralism and democracy. This study illustrates that the general public's opinions remain unheard-of on Twitter. Therefore, despite its potential to express a variety of opinions and voices, Twitter does not undermine the existing social order, but it is one of the cultural frameworks that help preserve the existing social order and reinforce it through uniform conformity thinking.Originality/valueThe novelty of this research lies in its identification and analysis of the characteristics of the Twitter scene, its meanings and its implications from the perspective of journalists. In addition, this research investigates the relationships forged in the Twitter space between journalists and politicians and among journalists themselves.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-07-2020-0324

Residencias artísticas virtuales en twitter : retos, motivaciones y experiencias.

A pesar de que el arte producido en Internet y para los usuarios de la Red no siempre goza de una publicidad y de un reconocimiento merecidos por parte de los canales de comunicación e instituciones artísticas hegemónicos, la producción de arte en el ciberespacio se ve fomentada gracias a la creación y desarrollo de residencias artísticas virtuales. Al igual que existen residencias físicas diseminadas por todo el planeta, existen residencias virtuales en los más diversos rincones del ciberespacio. La red social Twitter ha acogido tres programas de residencias en su seno con un alto grado de innovación y experimentación de los que apenas existe difusión. La presente investigación pretende ahondar en este territorio inexplorado ofreciendo un análisis crítico, pionero, de estas iniciativas mediante su presentación, el estudio de las condiciones e implicaciones del espacio de Twitter en el desarrollo de dichas prácticas y el estudio de las experiencias de los agentes implicados (organizadores y artistas participantes) con los que se ha contactado y remitido un cuestionario de preguntas abiertas. El conjunto de conclusiones obtenidas busca, más que confirmar una hipótesis, descubrir nuevas relaciones entre los planteamientos, objetivos, estímulos, posibilidades y desarrollos de estas residencias que deben ser tenidas en consideración tanto para el estudio de las nuevas prácticas artísticas en la red como para encarar el planteamiento y desarrollo de futuras iniciativas similares.

Hateful People or Hateful Bots?

Arabic Twitter space is crawling with bots that fuel political feuds, spread misinformation, and proliferate sectarian rhetoric. While efforts have long existed to analyze and detect English bots, Arabic bot detection and characterization remains largely understudied. In this work, we contribute new insights into the role of bots in spreading religious hatred on Arabic Twitter and introduce a novel regression model that can accurately identify Arabic language bots. Our assessment shows that existing tools that are highly accurate in detecting English bots don't perform as well on Arabic bots. We identify the possible reasons for this poor performance, perform a thorough analysis of linguistic, content, behavioral and network features, and report on the most informative features that distinguish Arabic bots from humans as well as the differences between Arabic and English bots. Our results mark an important step toward understanding the behavior of malicious bots on Arabic Twitter and pave the way for a more effective Arabic bot detection tools.

Investigating the effect of combining GRU neural networks with handcrafted features for religious hatred detection on Arabic Twitter space

Religious hatred is a serious problem on Arabic Twitter space and has the potential to ignite terrorism and hate crimes beyond cyber space. To the best of our knowledge, this is the first research effort investigating the problem of recognizing Arabic tweets using inflammatory and dehumanizing language to promote hatred and violence against people on the basis of religious beliefs. In this work, we create the first public Arabic dataset of tweets annotated for religious hate speech detection. We also create three public Arabic lexicons of terms related to religion along with hate scores. We then present a thorough analysis of the labeled dataset, reporting most targeted religious groups and hateful and non-hateful tweets’ country of origin. The labeled dataset is then used to train seven classification models using lexicon-based, n-gram-based, and deep-learning-based approaches. These models are evaluated on new unseen dataset to assess the generalization ability of the developed classifiers. While using Gated Recurrent Units with pre-trained word embeddings provides best precision (0.76) and $$F_1$$ score (0.77), training that same neural network on additional temporal, users, and content features provides the state-of-the-art performance in terms of recall (0.84).

Recommending Who to Follow in the Software Engineering Twitter Space

With the advent of social media, developers are increasingly using it in their software development activities. Twitter is one of the popular social mediums used by developers. A recent study by Singer et al. found that software developers use Twitter to “keep up with the fast-paced development landscape.” Unfortunately, due to the general-purpose nature of Twitter, it’s challenging for developers to use Twitter for their development activities. Our survey with 36 developers who use Twitter in their development activities highlights that developers are interested in following specialized software gurus who share relevant technical tweets. To help developers perform this task, in this work we propose a recommendation system to identify specialized software gurus. Our approach first extracts different kinds of features that characterize a Twitter user and then employs a two-stage classification approach to generate a discriminative model, which can differentiate specialized software gurus in a particular domain from other Twitter users that generate domain-related tweets (aka domain-related Twitter users). We have investigated the effectiveness of our approach in finding specialized software gurus for four different domains (JavaScript, Android, Python, and Linux) on a dataset of 86,824 Twitter users who generate 5,517,878 tweets over 1 month. Our approach can differentiate specialized software experts from other domain-related Twitter users with an F-Measure of up to 0.820. Compared with existing Twitter domain expert recommendation approaches, our proposed approach can outperform their F-Measure by at least 7.63%.

Twitter Users’ Privacy Concerns: What do Their Accounts’ First Names Tell Us?

Abstract Purpose In this paper, we describe how gender recognition on Twitter can be used as an intelligent business tool to determine the privacy concerns among users, and ultimately offer a more personalized service for customers who are more likely to respond positively to targeted advertisements. Design/methodology/approach We worked with two different data sets to examine whether Twitter users’ gender, inferred from the first name of the account and the profile description, correlates with the privacy setting of the account. We also used a set of features including the inferred gender of Twitter users to develop classifiers that predict user privacy settings. Findings We found that the inferred gender of Twitter users correlates with the account’s privacy setting. Specifically, females tend to be more privacy concerned than males. Users whose gender cannot be inferred from their provided first names tend to be more privacy concerned. In addition, our classification performance suggests that inferred gender can be used as an indicator of the user’s privacy preference. Research limitations It is known that not all twitter accounts are real user accounts, and social bots tweet as well. A major limitation of our study is the lack of consideration of social bots in the data. In our study, this implies that at least some percentage of the undefined accounts, that is, accounts that had names non-existent in the name dictionary, are social bots. It will be interesting to explore the privacy setting of social bots in the Twitter space. Practical implications Companies are investing large amounts of money in business intelligence tools that allow them to know the preferences of their consumers. Due to the large number of consumers around the world, it is very difficult for companies to have direct communication with each customer to anticipate market changes. For this reason, the social network Twitter has gained relevance as one ideal tool for information extraction. On the other hand, users’ privacy preference needs to be considered when companies consider leveraging their publicly available data. This paper suggests that gender recognition of Twitter users, based on Twitter users’ provided first names and their profile descriptions, can be used to infer the users’ privacy preference. Originality/value This study explored a new way of inferring Twitter user’s gender, that is, to recognize the user’s gender based on the provided first name and the user’s profile description. The potential of this information for predicting the user’s privacy preference is explored.

Message diffusion through social network service: The case of rumor and non-rumor related tweets during Boston bombing 2013

Social Network Services (SNS) such as Twitter play a significant role in reporting media, particularly during the extreme events. We examined the impact of tweet features on the diffusion of two types of messages during 2013 Boston marathon tragedy--rumor related and non-rumor related (both in the context of the Boston tragedy). Negative binomial analysis revealed that tweet features such as reaction time, number of followers, and usage of hashtag have an impact on tweet message diffusion during the tragedy. The number of followers showed a positive relationship with message diffusion. However, the relationship between tweet reaction time and message diffusion was negative. Finally, tweet messages that did not include hashtags diffused more than messages that contained hashtags. This paper contributes by adapting the innovation diffusion model to explore tweet message diffusion in Twitter space during extreme events.