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10.1192/j.eurpsy.2021.3 http://scihub22266oqcxt.onion/10.1192/j.eurpsy.2021.3 33531097!7943954!33531097     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Eur+Psychiatry 2021 ; 64 (1): e17 Nephropedia Template TP {{tp|p=33531097|t=2021. Surveilling COVID-19 Emotional Contagion on Twitter by Sentiment Analysis |pdf=|usr=}}{{33531097}} gab.com Text Surveilling COVID-19 Emotional Contagion on Twitter by Sentiment Analysis JO: Eur Psychiatry http://www.kidney.de/pget.php?&tw=1&pmid=33531097 #FOAvip BACKGROUND: The fight against the COVID-19 pandemic seems to encompass a social media debate, possibly resulting in emotional contagion and the need for novel surveillance approaches. In the current study, we aimed to examine the flow and content of tweets, exploring the role of COVID-19 key events on the popular Twitter platform. METHODS: Using representative freely available data, we performed a focused, social media-based analysis to capture COVID-19 discussions on Twitter, considering sentiment and longitudinal trends between January 19 and March 3, 2020. Different populations of users were considered. Core discussions were explored measuring tweets' sentiment, by both computing a polarity compound score with 95% Confidence Interval and using a transformer-based model, pretrained on a large corpus of COVID-19-related Tweets. Context-dependent meaning and emotion-specific features were considered. RESULTS: We gathered 3,308,476 tweets written in English. Since the first World Health Organization report (January 21), negative sentiment proportion of tweets gradually increased as expected, with amplifications following key events. Sentiment scores were increasingly negative among most active users. Tweets content and flow revealed an ongoing scenario in which the global emergency seems difficult to be emotionally managed, as shown by sentiment trajectories. CONCLUSIONS: Integrating social media like Twitter as essential surveillance tools in the management of the pandemic and its waves might actually represent a novel preventive approach to hinder emotional contagion, disseminating reliable information and nurturing trust. There is the need to monitor and sustain healthy behaviors as well as community supports also via social media-based preventive interventions. Twit Text FOAVip Surveilling COVID-19 Emotional Contagion on Twitter by Sentiment Analysis ????Eur Psychiatry http://www.kidney.de/pget.php?&tw=1&pmid=33531097 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33531097 #FOAvip English Wikipedia <ref>{{Cite pmid|33531097}}</ref> Surveilling COVID-19 Emotional Contagion on Twitter by Sentiment Analysis #MMPMID33531097 Crocamo C; Viviani M; Famiglini L; Bartoli F; Pasi G; Carra G Eur Psychiatry 2021[Feb]; 64 (1): e17 PMID33531097show ga BACKGROUND: The fight against the COVID-19 pandemic seems to encompass a social media debate, possibly resulting in emotional contagion and the need for novel surveillance approaches. In the current study, we aimed to examine the flow and content of tweets, exploring the role of COVID-19 key events on the popular Twitter platform. METHODS: Using representative freely available data, we performed a focused, social media-based analysis to capture COVID-19 discussions on Twitter, considering sentiment and longitudinal trends between January 19 and March 3, 2020. Different populations of users were considered. Core discussions were explored measuring tweets' sentiment, by both computing a polarity compound score with 95% Confidence Interval and using a transformer-based model, pretrained on a large corpus of COVID-19-related Tweets. Context-dependent meaning and emotion-specific features were considered. RESULTS: We gathered 3,308,476 tweets written in English. Since the first World Health Organization report (January 21), negative sentiment proportion of tweets gradually increased as expected, with amplifications following key events. Sentiment scores were increasingly negative among most active users. Tweets content and flow revealed an ongoing scenario in which the global emergency seems difficult to be emotionally managed, as shown by sentiment trajectories. CONCLUSIONS: Integrating social media like Twitter as essential surveillance tools in the management of the pandemic and its waves might actually represent a novel preventive approach to hinder emotional contagion, disseminating reliable information and nurturing trust. There is the need to monitor and sustain healthy behaviors as well as community supports also via social media-based preventive interventions. |*Emotions[MESH] |*Pandemics[MESH] |COVID-19/*epidemiology/prevention & control[MESH] |Health Behavior[MESH] |Health Education[MESH] |Humans[MESH] |Longitudinal Studies[MESH] |SARS-CoV-2[MESH] |Social Media/*statistics & numerical data[MESH]Surveilling COVID-19 Emotional Contagion on Twitter by Sentiment Analy(epmc).pdf DeepDyve Pubget Overpricing