Negative emotions shape the diffusion of cancer tweets: toward an integrated social network–text analytics approach

Wang, Xiaohui; Lee, Edmund Wei Jian

doi:10.1108/intr-04-2020-0181

Cited by 23 publications

(18 citation statements)

References 71 publications

(95 reference statements)

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“…However, the addition of one or more neutral emotions such as surprise, trust, and anticipation led to lower diffusion results. Looking at the other independent factors, we can see that follower, URL, and media had significant impact on all of the diffusion constructs, as found in other studies [5][45] [44].…”

Section: Findings and Limitationssupporting

confidence: 78%

“…Another research by Wang & Wei [43] concluded that anger is positively correlated to information diffusion for cancer-related discussions. Very recent research by Wang and Lee [44] also analyzed the impact of negative emotions on cancer tweet diffusion. Meanwhile, other studies have discovered that negative emotions do not widely circulate general knowledge [25], political [5], or health-related signals [39].…”

Section: Emotions and Information Diffusionmentioning

confidence: 99%

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Impact of emotions in social media content diffusion

Chawla

Mehrotra

2021

IJCAI

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Emotions present in social media content shape its diffusion. This study seeks to comprehensively examine the impact created by emotions of a social media message on its diffusion. Centered on a non-domain specific Twitter dataset, the authors define several measurement constructs to quantify the tweet diffusion process namely, speed, size, half-life, diffusion potential, and engagement. Since a message may express a single dominant emotion or multiple categories of emotions, the current study focuses to investigate the influence of emotions in the single label as well as multi-label setting. Through extensive statistical analyses (Multivariate Analysis of Variance and Regression), we find that the impact of emotions on diffusion constructs was statistically significant. The findings shed light on how emotions aid or hinder the spread of information through social media. Specifically, the tweets containing joy or contempt as primary emotion attained faster and stronger diffusion. In contrast, anger or fear as primary emotion in tweets contributed to slower and weaker diffusion. Also, the combination of one or more positive and negative emotions increased the diffusion outcome.Povzetek: Analiziran je vpliv pozitivnih in negativnih čustev na hitrost razširjanje čivka oz. tvita.

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Section: Findings and Limitationssupporting

confidence: 78%

Section: Emotions and Information Diffusionmentioning

confidence: 99%

Impact of emotions in social media content diffusion

Chawla

Mehrotra

2021

IJCAI

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“…Secondly, the emotion communication capacity usually decreased as the emotion tendency changed from positive to negative. Previous studies have shown that positive emotions reduce people’s attention [ 62 , 63 , 80 ] and that disgust and fear spread more widely through social media [ 70 , 71 , 72 ], but the results of this study are not consistent with these notions. This is mainly because food safety is an issue that is close to people’s livelihood.…”

Section: Discussioncontrasting

confidence: 99%

“…Negative emotions mediated the level of misinformation and information transmission [ 58 ]. Compared with neutral emotions, negative emotions that may cause disgust, fear, or happiness are more likely to spread from person to person, and they are spread more widely through social media [ 70 , 71 , 72 ]. Negative emotions tend to cause people to pay more attention, while there is less focus on positive emotions [ 73 , 74 ].…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Social Media Users’ Emotions Regarding Food Safety Issues: Content Analysis of a Debate among Chinese Weibo Users on Genetically Modified Food Security

Xiong

2021

Healthcare

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Social media is gradually building an online information environment regarding health. This environment is filled with many types of users’ emotions regarding food safety, especially negative emotions that can easily cause panic or anger among the population. However, the mechanisms of how it affects users’ emotions have not been fully studied. Therefore, from the perspective of communication and social psychology, this study uses the content analysis method to analyze factors affecting social media users’ emotions regarding food safety issues. In total, 371 tweet samples of genetically modified food security in Sina Weibo (similar to Twitter) were encoded, measured, and analyzed. The major findings are as follows: (1) Tweet account type, tweet topic, and emotion object were all significantly related to emotion type. Tweet depth and objectivity were both positively affected by emotion type, and objectivity had a greater impact. (2) Account type, tweet topic, and emotion object were all significantly related to emotion intensity. When the depths were the same, emotion intensity became stronger with the decrease in objectivity. (3) Account type, tweet topic, emotion object, and emotion type were all significantly related to a user’s emotion communication capacity. Tweet depth, objectivity, and user’s emotion intensity were positively correlated with emotion communication capacity. Positive emotions had stronger communication capacities than negative ones, which is not consistent with previous studies. These findings help us to understand both theoretically and practically the changes and dissemination of user’s emotions in a food safety and health information environment.

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“…As Twitter has evolved to become a widely used and legitimated source of news and information, its use during times of crises has received considerable attention from scholars and practitioners in recent years (Sutton et al, 2020 ). Previous studies have mainly examined three types of message‐related factors influencing crisis information sharing on Twitter, namely, Twitter characteristics such as the inclusion of hashtags and/or hyperlinks (e.g., Son et al, 2020 ; Sutton et al, 2014 , 2020 ; Sutton, Gibson, et al, 2015 ), tweet emotions (Wang & Lee, 2020 ; Xu & Zhang, 2018 ), and tweet content themes (Chew & Eysenbach, 2010 ; Son et al, 2019 ; Sutton, Ben Gibson, et al, 2015 ). Collectively, these message features can be viewed as drivers or barriers of retransmission of crisis information, depending on the specific context.…”

Section: Literature Review and Theoretical Foundationsmentioning

confidence: 99%

Understanding the effects of message cues on COVID‐19 information sharing on Twitter

Zheng

Goh

Lee

et al. 2021

Asso for Info Science & Tech

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Analyzing and documenting human information behaviors in the context of global public health crises such as the COVID‐19 pandemic are critical to informing crisis management. Drawing on the Elaboration Likelihood Model, this study investigates how three types of peripheral cues—content richness, emotional valence, and communication topic—are associated with COVID‐19 information sharing on Twitter. We used computational methods, combining Latent Dirichlet Allocation topic modeling with psycholinguistic indicators obtained from the Linguistic Inquiry and Word Count dictionary to measure these concepts and built a research model to assess their effects on information sharing. Results showed that content richness was negatively associated with information sharing. Tweets with negative emotions received more user engagement, whereas tweets with positive emotions were less likely to be disseminated. Further, tweets mentioning advisories tended to receive more retweets than those mentioning support and news updates. More importantly, emotional valence moderated the relationship between communication topics and information sharing—tweets discussing news updates and support conveying positive sentiments led to more information sharing; tweets mentioning the impact of COVID‐19 with negative emotions triggered more sharing. Finally, theoretical and practical implications of this study are discussed in the context of global public health communication.

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Negative emotions shape the diffusion of cancer tweets: toward an integrated social network–text analytics approach

Cited by 23 publications

References 71 publications

Impact of emotions in social media content diffusion

Impact of emotions in social media content diffusion

Factors Affecting Social Media Users’ Emotions Regarding Food Safety Issues: Content Analysis of a Debate among Chinese Weibo Users on Genetically Modified Food Security

Understanding the effects of message cues on COVID‐19 information sharing on Twitter

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