Exploring the impact of sentiment on multi-dimensional information dissemination using COVID-19 data in China

Luo, Han; Xiao, Meng; Zhao, Yifei; Cai, Meng

doi:10.1016/j.chb.2023.107733

Cited by 15 publications

(16 citation statements)

References 81 publications

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“…Zhao et al (2021) identified health misinformation based on topics, linguistic features, emotional features, and user behavioral features. Luo et al (2023) investigated the influence of sentiment on information diffusion during COVID-19 and found that negative information could significantly promote the volume, depth, and influence of information transmission. The ELM presents a suitable theoretical lens for elucidating the information attributes that influence the diffusion of misinformation.…”

Section: Factors Influencing the Spread Of Misinformationmentioning

confidence: 99%

“…2.2.1 Emotion. Emotion orients people to respond to ongoing events in their environment (Keltner and Lerner, 2010), and it is considered one of the most important factors promoting the diffusion of online information (Luo et al, 2023;Mir et al, 2021;Weismueller et al, 2022). According to the ELM, emotions can act as peripheral cues to have arousing effects that drive attention and attitude change (Xu and Zhang, 2018;Zhao et al, 2022).…”

Section: Factors Influencing the Spread Of Misinformationmentioning

confidence: 99%

“…BERT is a pre-trained deep-learning language model developed by Google AI, which can generate word vectors containing rich semantic information (Devlin et al, 2018). Researchers commonly fine-tune the pre-trained BERT model by adding an output layer, thus enabling BERT to perform various tasks (Cai et al, 2022;Luo et al, 2023). In this study, we fine-tuned the BERT model using the emotion classification data released by the CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC), where the microblogs are labeled into eight emotion categories.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

“…Often, users with a larger number of followers act as opinion leaders and influence public opinion and behavior (Wang et al, 2020b). In addition, on Weibo, verified users tend to have extensive experience in their professional fields and are considered more authoritative LHT 42,2 (Luo et al, 2023;Mir et al, 2021). Therefore, we considered the number of followers and whether they are verified to measure the authority.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

“…In this study, we also introduced control variables to eliminate the interference of other factors in the results. According to previous research (Cai et al, 2022;Lee et al, 2014;Luo et al, 2023;Meng et al, 2018;Zhou et al, 2021a), multiple factors may interfere with the results. For example, adding hashtags to a microblog can spread information not only to the followers of the microblog publisher but also to users who follow a particular hashtag.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

See 4 more Smart Citations

Depth, breadth and structural virality: the influence of emotion, topic, authority and richness on misinformation spread

Meng,

Dai,

Zhao

et al. 2023

LHT

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PurposeThis study aims to investigate the mechanism of the misinformation spread based on the elaboration likelihood model and the effects of four factors – emotion, topic, authority and richness – on the depth, breadth and structural virality of misinformation spread.Design/methodology/approachThe authors collected 2,514 misinformation microblogs and 142,006 reposts from Weibo, used deep learning methods to identify the emotions and topics of misinformation and extracted the structural characteristics of the spreading network using the network analysis method.FindingsResults show that misinformation has a smaller spread size and breadth than true news but has a similar spread depth and structural virality. The differential influence of emotions on the structural characteristics of misinformation propagation was found: sadness can promote the breadth of misinformation spread, anger can promote depth and disgust can promote depth and structural virality. In addition, the international topic, the number of followers, images and videos can significantly and positively influence the misinformation's spread size, depth, breadth and structural virality.Originality/valueThe influencing factors of the structural characteristics of misinformation propagation are clarified, which is helpful for the detection and management of misinformation.

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Section: Factors Influencing the Spread Of Misinformationmentioning

confidence: 99%

Section: Factors Influencing the Spread Of Misinformationmentioning

confidence: 99%

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Section: Data Collection and Preprocessingmentioning

confidence: 99%

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Depth, breadth and structural virality: the influence of emotion, topic, authority and richness on misinformation spread

Meng,

Dai,

Zhao

et al. 2023

LHT

Self Cite

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A roadmap to achieving a healthier information ecosystem through GDPR implementation and privacy compliance technologies

Li,

Xiong,

Yang

2024

Asso for Info Science & Tech

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Privacy protection has become a central issue in information science, with the General Data Protection Regulation (GDPR) significantly impacting information ecosystems. Research gaps persist in understanding the causal relationship between GDPR implementation and websites' proactive changes, such as adopting privacy compliance technologies. This study aims to examine the influence of GDPR implementation on websites' information ecosystems and identify boundary conditions that may affect this relationship. Utilizing domain‐level data from a professional platform tracking website technologies before and after GDPR implementation, which encompasses a comprehensive longitudinal dataset of over 1.2 million websites, our results indicate that GDPR implementation has increased the decision, breadth, depth, and intensity of adopting related technologies. The variance in these adoptions is significantly shaped by the differing levels of institutional impacts experienced by trailblazing versus laggard industries. Our study contributes to the literature by revealing the importance of policy factors in fostering a healthier information ecosystem. We demonstrate the complex nature of the relationship between policy factors and technology adoption, highlighting the need for researchers to consider institutional context and industry‐specific characteristics. We further discuss valuable insights for relevant stakeholders.

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Study on the Evolution of Public Opinion on Public Health Events

Liu,

Hu,

Yue

2024

Communications in Computer and Information Science

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Exploring the impact of sentiment on multi-dimensional information dissemination using COVID-19 data in China

Cited by 15 publications

References 81 publications

Depth, breadth and structural virality: the influence of emotion, topic, authority and richness on misinformation spread

Depth, breadth and structural virality: the influence of emotion, topic, authority and richness on misinformation spread

A roadmap to achieving a healthier information ecosystem through GDPR implementation and privacy compliance technologies

Study on the Evolution of Public Opinion on Public Health Events

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