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BackgroundDuring the COVID-19 pandemic, a number of conspiracy theories have emerged. A popular theory posits that the pandemic is a hoax and suggests that certain hospitals are “empty.” Research has shown that accepting conspiracy theories increases the likelihood that an individual may ignore government advice about social distancing and other public health interventions. Due to the possibility of a second wave and future pandemics, it is important to gain an understanding of the drivers of misinformation and strategies to mitigate it. ObjectiveThis study set out to evaluate the #FilmYourHospital conspiracy theory on Twitter, attempting to understand the drivers behind it. More specifically, the objectives were to determine which online sources of information were used as evidence to support the theory, the ratio of automated to organic accounts in the network, and what lessons can be learned to mitigate the spread of such a conspiracy theory in the future. MethodsTwitter data related to the #FilmYourHospital hashtag were retrieved and analyzed using social network analysis across a 7-day period from April 13-20, 2020. The data set consisted of 22,785 tweets and 11,333 Twitter users. The Botometer tool was used to identify accounts with a higher probability of being bots. ResultsThe most important drivers of the conspiracy theory are ordinary citizens; one of the most influential accounts is a Brexit supporter. We found that YouTube was the information source most linked to by users. The most retweeted post belonged to a verified Twitter user, indicating that the user may have had more influence on the platform. There was a small number of automated accounts (bots) and deleted accounts within the network. ConclusionsHashtags using and sharing conspiracy theories can be targeted in an effort to delegitimize content containing misinformation. Social media organizations need to bolster their efforts to label or remove content that contains misinformation. Public health authorities could enlist the assistance of influencers in spreading antinarrative content.

BackgroundRespecting patient privacy and confidentiality is critical for doctor-patient relationships and public trust in medical professionals. The frequency of potentially identifiable disclosures online during periods of active engagement is unknown. ObjectiveThe objective of this study was to quantify potentially identifiable content shared on social media by physicians and other health care providers using the hashtag #ShareAStoryInOneTweet. MethodsWe accessed and searched Twitter’s API using Symplur software for tweets that included the hashtag #ShareAStoryInOneTweet. We identified 1206 tweets by doctors, nurses, and other health professionals out of 43,374 tweets shared in May 2018. Tweet content was evaluated in January 2019 to determine the incidence of instances where names or potentially identifiable information about patients were shared; content analysis of tweets in which information about others had been disclosed was performed. The study also evaluated whether participants raised concerns about privacy breaches and estimated the frequency of deleted tweets. The study used dual, blinded coding for a 10% sample to estimate intercoder reliability using Cohen κ statistic for identifying the potential identifiability of tweet content. ResultsHealth care professionals (n=656) disclosing information about others included 486 doctors (74.1%) and 98 nurses (14.9%). Health care professionals sharing stories about patient care disclosed the time frame in 95 tweets (95/754, 12.6%) and included patient names in 15 tweets (15/754, 2.0%). It is estimated that friends or families could likely identify the clinical scenario described in 242 of the 754 tweets (32.1%). Among 348 tweets about potentially living patients, it was estimated that 162 (46.6%) were likely identifiable by patients. Intercoder reliability in rating the potential identifiability demonstrated 86.8% agreement, with a Cohen κ of 0.8 suggesting substantial agreement. We also identified 78 out of 754 tweets (6.5%) that had been deleted on the website but were still viewable in the analytics software data set. ConclusionsDuring periods of active sharing online, nurses, physicians, and other health professionals may sometimes share more information than patients or families might expect. More study is needed to determine whether similar events arise frequently and to understand how to best ensure that patients’ rights are adequately respected.

BackgroundSince the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it. ObjectiveThe aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation. MethodsThis paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph’s vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined. ResultsSocial network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy. This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter. ConclusionsThe combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future.

Research question: Given the worldwide growth of women's football and its presence on social media, it is essential to explore and understand fan attitudes and culture. Research methods: This article provides the first empirical social media netnography focusing on English women's football teams (Manchester United and Burnley) and international fan views towards women professional players on TikTok. We extend this discussion by utilising a netnography in which researchers immersed themselves for seven months in women's football groups on TikTok to gather and analyse new qualitative data in this context. Results and Findings: We identify the escalation of gender-based violence on social media against women players. Four key themes emerged from the netnography: 1. Sexism: the place of women in football; 2. Misogyny and hatred of women; 3. Sexualisation of women; 4. Demand for a male-only space. Sexist comments were apparent in all of the TikTok posts containing female football players, with some also containing more aggressive misogynistic comments. Other dominant comments sought to reduce women to objects of sexual desire and belittle their professional skills, whereas others were appalled at the presence of female players on the clubs' official accounts, demanding them to be a male-only space. Implications: The study contributes to the understanding of online fan cultures on complex, video-based platforms such as TikTok. Through literature review and netnography, we identified a problem for football clubs on social media of longstanding, problematic issues of toxic fan comments. [ABSTRACT FROM AUTHOR]

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Understanding the salience of university attributes assists institutions in developing messaging strategies (using social responsibility communication (SRC) initiatives) to meet students' needs. This research examines which university attributes hold the greatest significance and importance for students selecting a higher-education institution (HEI), focusing on the role of SRC as a mediator. An online survey with a sample of 120 university students was conducted. The findings indicate that the cost of education, employment opportunities, physical aspects and resources, and university image are important attributes. The study shows that while university attributes significantly predict SRC (Model 1), neither university attributes nor SRC significantly predicts student attitudes (Models 2 and 3). The mediation analysis confirms that SRC does not mediate the relationship between university attributes and students' attitudes, suggesting that factors other than university attributes and SRC may play a more critical role in shaping students' attitudes. [ABSTRACT FROM AUTHOR]

Let S be a prime ring with automorphisms α,β. A bi-additive map D is called an (α,β) Jordan bi-derivation if D(k2,s)=D(k,s)α(k)+β(k)D(k,s). In this paper, we find conditions under which a symmetric (α,β) Jordan bi-derivation becomes a symmetric (α,β) bi-derivation. We also characterize the symmetric (α,β) Jordan bi-derivations. [ABSTRACT FROM AUTHOR]