Fear and Hope, Bitter and Sweet: Emotion Sharing of Cancer Community on Twitter

Abstract

Emotions are non-negligible parts of the experience among the cancer-affected population to be reckoned with. With the increasing usage of social media platforms as venues for emotional disclosure, we ask the question, what and how are the emotions of the cancer community being shared there? Using a deep learning model and social network analysis, we investigated emotions expressed in a large collection of cancer-related tweets. The results showed that joy was the most commonly shared emotion, followed by sadness and fear, with anger, hope, and bittersweet being less shared. In addition, both the gatekeepers and influencers were more likely to post content with positive emotions, while gatekeepers refrained themselves from posting negative emotions to a greater extent. Last, cancer-related tweets with joy, sadness, and hope received more likes, whereas tweets with joy and anger were more retweeted. The implications of the findings are discussed in the context of social media health communities.

Natural Language Processing–Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study

“…The AICF has incorporated clinically trained therapists to identify distress and clinical outcomes from the data while most studies relied on hashtags to classify topics and emotions or on laymen annotators [ 55 ]. In one of the few studies [ 57 ] that attempted to identify emotions in a cancer population [ 57 ] using recurrent neural network models to extract common basic emotions such as fear and hope from tweets by patients with cancer, the authors identified joy as the most commonly shared emotion, followed by sadness and fear; these findings are similar to ours. However, the AICF will contribute to the uncharted areas of clinical psychology and psychiatry in which automatic emotion detection and classification systems have not yet been fully explored.…”

Tweets and Quacks: Network and Content Analyses of Providers of Non-Science-Based Anticancer Treatments and Their Supporters on Twitter

“…Previous analyses on supporters' roles and attitudes on other diseases (for instance, those which might benefit from stem-cell therapy, Lavorgna & Di Ronco, 2017) suggest that certain online dynamics (such as motivations and general attitudes) are likely to be similar. Nonetheless, because of the unique nature of fear and fatalism related to cancer, especially in some demographics (Almeida et al, 2019;Curran et al, 2020), and the gatekeepers and influencers' role in the Twitter cancer community (Wang & Wei, 2020), we would expect, for instance, to see a more defined group (rather than transient crowds of strangers) pivoting around, for instance, a rarer disease. Further research, therefore, is needed before our findings can be safely generalized.…”

Corona and value change. The role of social media and emotional contagion

“…Indeed, on social media emotional information spreads more quickly than information that is not related to emotions. For instance, Twitter messages about cancer that included joy, sadness, and hope are liked more than others, and tweets that contain joy and anger are retweeted more than others (Wang and Wei 2020 ). Furthermore, the presence of emotional-moral words in social media messages increase their spreading substantially (Brady et al 2017 ), and digital media platforms seem to exacerbate content that induces outrage (Crockett 2017 ).…”