Could Social Bots’ Sentiment Engagement Shape Humans’ Sentiment on COVID-19 Vaccine Discussion on Twitter?

Zhang, Menghan; Chen, Ze; Qin, Xue; Liu, Jun

doi:10.3390/su14095566

Cited by 7 publications

(4 citation statements)

References 71 publications

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“…Studies also have shown that social media users prefer to exchange information with users in the same situation as themselves (Chan-Olmsted et al, 2013). Relying on the deep integration of artificial intelligence and social media, social bots have a stronger ability to hide and imitate, making it completely impossible for the public to recognize them with the naked eye (Zhang, Chen, et al, 2022). For the public, social bots that masquerade as regular users are their trusted social media friends.…”

Section: Discussionmentioning

confidence: 99%

Who Leads? Who Follows? Exploring Agenda Setting by Media, Social Bots and Public in the Discussion of the 2022 South Korean Presidential Election

Zhang,

Qi,

Liu

et al. 2024

Sage Open

Self Cite

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Social media not only changes the traditional communication environment but also brings new changes to agenda-setting. The main body of agenda-setting has shifted from the traditional media to the politicians, political parties and grassroots people. With the increasing use of social bots in public opinion manipulation and political election interference, whether they can participate in or influence agenda-setting has become an urgent concern. So far, there has been less literature focusing on engagement in agenda-setting for social bots. This paper studies the social media discussion content of the South Korean presidential election, determines the participation of social bots, and explores the connection between media agenda, bot agenda, and public agenda from the perspective of agenda setting. This study finds that while the main agendas of media, social bots, and the public are not the same, their agendas are relevant. In addition, the media agenda is not timely ahead of the bot agenda and the public agenda, and the time order only appears between the social bots and the public.

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Section: Discussionmentioning

confidence: 99%

Who Leads? Who Follows? Exploring Agenda Setting by Media, Social Bots and Public in the Discussion of the 2022 South Korean Presidential Election

Zhang,

Qi,

Liu

et al. 2024

Sage Open

Self Cite

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“…In this sense, some studies analyzed the role of bots regarding the spread of misinformation in general, while others have focused specifically on topics such as vaccines, conspiracy theories, hate speech, or reactions to other political actions [25][26][27][28][29][30][31]. However, a small amount of research compared the behavior of bots and humans [32,33].…”

Section: Unraveling the Use Of Disinformation Hashtags By Social Bots...mentioning

confidence: 99%

Unraveling the use of disinformation hashtags by social bots during the Covid-19 pandemic: a networks analysis and community detection (Preprint)

Suarez-Lledo,

Ortega-Martin,

Carretero-Bravo

et al. 2023

JMIR Infodemiology

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Background: During the COVID-19 pandemic, social media platforms have been a venue for the exchange of messages, including those related to fake news. There are also accounts programmed to disseminate and amplify specific messages, which can affect individual decision-making and present new challenges for public health.Objective: This paper aims to analyze how social bots use hashtags compared to human users on topics related to misinformation during the outbreak of the Covid-19 pandemic. Methods:We selected tweets on specific topics related to infodemics such as vaccines, hydroxychloroquine, military, conspiracy, laboratory, Bill Gates, 5G, and UV. We built a network based on the co-occurrence of hashtags and classified the tweets based on their source. Using network analysis and community detection algorithms, we identified hashtags that tend to appear together in messages. For each topic, we extracted the most relevant subtopic communities, which are groups of interconnected hashtags. Results:The distribution of bots and nonbots in each of these communities was uneven, with some sets of hashtags being more common among accounts classified as bots or nonbots. Hashtags related to the Trump and Qanon social movements were common among bots, and specific hashtags with anti-Asian sentiments were also identified. In the subcommunities most populated by bots in the case of vaccines, the group of hashtags including #billgates, #pandemic, and #china was among the most common. Conclusions:The use of certain hashtags varies depending on the source, and some hashtags are used for different purposes. Understanding these patterns may help address the spread of health misinformation on social media networks.

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“…The BERT model also has an obvious advantage in sentiment analysis tasks [109]. Furthermore, optimized BERT and BERT-CNN models can even achieve about a 10% higher accuracy than LSTM (or Bi-LSTM) in sentiment analysis tasks [110][111][112]. However, BiLSTM models can achieve significantly higher results than the BERT model using a small dataset [113].…”

Section: Natural Language Processing and Text Miningmentioning

confidence: 99%

Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review

Yang

Liu

2022

Land

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Social media data have been widely used in natural sciences and social sciences in the past 5 years, benefiting from the rapid development of deep learning frameworks and Web 2.0. Its advantages have gradually emerged in urban design, urban planning, landscape architecture design, sustainable tourism, and other disciplines. This study aims to obtain an overview of social media data in urban design and landscape research through literature reviews and bibliometric visualization as a comprehensive review article. The dataset consists of 1220 articles and reviews works from SSCI, SCIE, and A&HCI, based on the Web of Science core collection, respectively. The research progress and main development directions of location-based social media, text mining, and image vision are introduced. Moreover, we introduce Citespace, a computer-network-based bibliometric visualization, and discuss the timeline trends, hot burst keywords, and research articles with high co-citation scores based on Citespace. The Citespace bibliometric visualization tool facilitates is used to outline future trends in research. The literature review shows that the deep learning framework has great research potential for text emotional analysis, image classification, object detection, image segmentation, and the expression classification of social media data. The intersection of text, images, and metadata provides attractive opportunities as well.

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Could Social Bots’ Sentiment Engagement Shape Humans’ Sentiment on COVID-19 Vaccine Discussion on Twitter?

Cited by 7 publications

References 71 publications

Who Leads? Who Follows? Exploring Agenda Setting by Media, Social Bots and Public in the Discussion of the 2022 South Korean Presidential Election

Who Leads? Who Follows? Exploring Agenda Setting by Media, Social Bots and Public in the Discussion of the 2022 South Korean Presidential Election

Unraveling the use of disinformation hashtags by social bots during the Covid-19 pandemic: a networks analysis and community detection (Preprint)

Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review

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