scholarly journals Cognitive Network Science Reconstructs How Experts, News Outlets and Social Media Perceived the COVID-19 Pandemic

Systems ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 38
Author(s):  
Massimo Stella
Keyword(s):  
Social Media ◽  
Language Processing ◽  
News Media ◽  
Network Science ◽  
Wide Spectrum ◽  
Emotional States ◽  
Cognitive Network ◽  
Public Speech ◽  
Health Crisis ◽  
Semantic Associations

This work uses cognitive network science to reconstruct how experts, influential news outlets and social media perceived and reported the news “COVID-19 is a pandemic”. In an exploratory corpus of 1 public speech, 10 influential news media articles on the same news and 37,500 trending tweets, the same pandemic declaration elicited a wide spectrum of perceptions retrieved by automatic language processing. While the WHO adopted a narrative strategy of mitigating the pandemic by raising public concern, some news media promoted fear for economic repercussions, while others channelled trust in contagion containment through semantic associations with science. In Italy, the first country to adopt a nationwide lockdown, social discourse perceived the pandemic with anger and fear, emotions of grief elaboration, but also with trust, a useful mechanism for coping with threats. Whereas news mostly elicited individual emotions, social media promoted much richer perceptions, where negative and positive emotional states coexisted, and where trust mainly originated from politics-related jargon rather than from science. This indicates that social media linked the pandemics to institutions and their intervention policies. Since both trust and fear strongly influence people’s risk-averse behaviour and mental/physical wellbeing, identifying evidence for these emotions is key under a global health crisis. Cognitive network science opens the way to unveiling the emotional framings of massively read news in automatic ways, with relevance for better understanding how information was framed and perceived by large audiences.

scholarly journals Cognitive network science reconstructs how experts, news outlets and social media perceived the COVID-19 pandemic

2020 ◽  
Author(s):  
Massimo Stella
Keyword(s):  
Social Media ◽  
Language Processing ◽  
News Media ◽  
Network Science ◽  
Wide Spectrum ◽  
Emotional States ◽  
Cognitive Network ◽  
Public Speech ◽  
Health Crisis ◽  
Semantic Associations

This work uses cognitive network science to reconstruct how experts, influential news outlets and social media perceived and reported the news “COVID-19 is a pandemic”. In an exploratory corpus of 1 public speech, 10 influential news media articles on the same news and 37,500 trending tweets, the same pandemic declaration elicited a wide spectrum of perceptions retrieved by automatic language processing. While the WHO adopted a narrative strategy of mitigating the pandemic by raising public concern, some news media promoted fear for economic repercussions while others channelled trust in contagion containment through semantic associations with science (e.g. “flatten the curve”), a strategy found also in other studies. In Italy, the first country to adopt nationwide lockdown, social discourse perceived the pandemic with anger and fear, emotions of grief elaboration, but also with trust, a useful mechanism for coping with threats. Whereas news mostly elicited individual emotions, social media promoted much richer perceptions, where negative and positive emotional states coexisted, but also where trust mainly originated from politics-related jargon rather than from science. This indicates that social media linked the pandemics to institutions and their intervention policies. The above emotions strongly influence people’s risk-averse behaviour and mental/physical wellbeing, as reviewed here in view of recent work about COVID-19. Cognitive network science opens the way to unveiling and understanding emotional framings and perceptions of news, with relevance for better understanding information flow during a global health crisis with little human coding.

scholarly journals Revealing semantic and emotional structure of suicide notes with cognitive network science

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andreia Sofia Teixeira ◽  
Szymon Talaga ◽  
Trevor James Swanson ◽  
Massimo Stella
Keyword(s):  
Suicidal Ideation ◽  
Network Science ◽  
Meaning Making ◽  
Null Model ◽  
Future Research ◽  
Emotional States ◽  
Cognitive Network ◽  
Cognitive States ◽  
Suicide Notes ◽  
Scientific Challenge

AbstractUnderstanding how people who commit suicide perceive their cognitive states and emotions represents an important open scientific challenge. We build upon cognitive network science, psycholinguistics and semantic frame theory to introduce a network representation of suicidal ideation as expressed in multiple suicide notes. By reconstructing the knowledge structure of such notes, we reveal interconnections between the ideas and emotional states of people who committed suicide through an analysis of emotional balance motivated by structural balance theory, semantic prominence and emotional profiling. Our results indicate that connections between positively- and negatively-valenced terms give rise to a degree of balance that is significantly higher than in a null model where the affective structure is randomized and in a linguistic baseline model capturing mind-wandering in absence of suicidal ideation. We show that suicide notes are affectively compartmentalized such that positive concepts tend to cluster together and dominate the overall network structure. Notably, this positive clustering diverges from perceptions of self, which are found to be dominated by negative, sad conceptual associations in analyses based on subject-verb-object relationships and emotional profiling. A key positive concept is “love”, which integrates information relating the self to others and is semantically prominent across suicide notes. The emotions constituting the semantic frame of “love” combine joy and trust with anticipation and sadness, which can be linked to psychological theories of meaning-making as well as narrative psychology. Our results open new ways for understanding the structure of genuine suicide notes and may be used to inform future research on suicide prevention.

scholarly journals SMARTS: the social media-based addiction recovery and intervention targeting server

2019 ◽  
Author(s):  
Deeptanshu Jha ◽  
Rahul Singh
Keyword(s):  
Social Media ◽  
Real Time ◽  
Language Processing ◽  
Drug Users ◽  
Supplementary Information ◽  
Addiction Recovery ◽  
Time Data ◽  
Health Crisis ◽  
Effective Interventions ◽  
The Individual

Abstract Motivation Substance abuse and addiction is a significant contemporary health crisis. Modeling its epidemiology and designing effective interventions requires real-time data analysis along with the means to contextualize addiction patterns across the individual-to-community scale. In this context, social media platforms have begun to receive significant attention as a novel source of real-time user-reported information. However, the ability of epidemiologists to use such information is significantly stymied by the lack of publicly available algorithms and software for addiction information extraction, analysis and modeling. Results SMARTS is a public, open source, web-based application that addresses the aforementioned deficiency. SMARTS is designed to analyze data from two popular social media forums, namely, Reddit and Twitter and can be used to study the effect of various intoxicants including, opioids, weed, kratom, alcohol and cigarettes. The SMARTS software analyzes social media posts using natural language processing, and machine learning to characterize drug use at both the individual- and population-levels. Included in SMARTS is a predictive modeling functionality that can, with high accuracy, identify individuals open to addiction recovery interventions. SMARTS also supports extraction, analysis and visualization of a number of key informational and demographic characteristics including post topics and sentiment, drug- and recovery-term usage, geolocation and age. Finally, the distributions of the aforementioned characteristics as derived from a set of 170 097 drug users are provided as part of SMARTS and can be used by researchers as a reference. Availability and implementation The SMARTS web server and source code are available at: http://haddock9.sfsu.edu/. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals COVID-19 news reporting and engaging in the age of social media: Comparing Xinhua News Agency and The Paper

2021 ◽  
pp. 205943642110173
Author(s):  
Zenan Chen ◽  
Xiaoge Xu
Keyword(s):  
Social Media ◽  
News Media ◽  
Special Form ◽  
User Engagement ◽  
Health Crisis ◽  
New Normal ◽  
News Reporting ◽  
News Stories ◽  
News Agency ◽  
Xinhua News Agency

“To follow and to be followed” has become the new normal in news communication in the age of social media. News audience follow news via social media while they are being followed by news anytime anywhere. This new normal has created a pressing need to investigate whether social media have brought any changes to both party-controlled and market-oriented news media in China in reporting crises. Comparing Xinhua News Agency (party-controlled) and The Paper (market-oriented), this study investigated how they reported COVID-19 and how their news consumers engaged with their COVID-19 news stories on Jinri Toutiao, a popular and yet special form of social media. This study found that Xinhua News Agency continued to stay overwhelmingly positive, while The Paper was more neutral in reporting the health crisis. Xinhua News Agency was surprisingly more episodic than The Paper in framing the pandemic. The Paper, however, had a higher level of user engagement than Xinhua News Agency. To cater to the changing news-seeking behaviors and patterns, both party-controlled and market-oriented news media have changed their operations, but not their fundamental orientations.

scholarly journals Emotions of COVID-19: A Study of Self-Reported Information and Emotions during the COVID-19 Pandemic using Human Centric Artificial Intelligence (Preprint)

2021 ◽  
Author(s):  
Achini Adikari ◽  
Rashmika Nawaratne ◽  
Daswin De Silva ◽  
Sajani Ranasinghe ◽  
Oshadi Alahakoon ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Language Processing ◽  
Human Life ◽  
Significant Loss ◽  
State Transitions ◽  
Clustering Methods ◽  
Health Crisis ◽  
Use Of Social Media ◽  
Over Time

BACKGROUND The COVID-19 pandemic has caused a global disruption, starting with a public health emergency, followed by a significant loss of human life and a severe economic and social fallout. As physical distancing regulations were introduced to manage outbreaks, individuals, groups and communities took to social media to express their thoughts and emotions reflecting different behaviours. This has led to increased interaction on social media thereby recording diverse behaviours of people as the pandemic progressed. OBJECTIVE This research aims to explore the human behaviours recorded on the digital traces of social media during the pandemic. The investigation is focused on examining the emotions, emotion state and intensity changes, topical associations and different groups of people using social media conversations that uncover informed insights on behaviours during the COVID-19 pandemic. METHODS Our study explores emotion classifications, intensities, transitions, profiles and alignment to key themes and topics, across the four stages of the pandemic; declaration of a global health crisis, first lockdown, easing of restrictions, and the second lockdown. This study employs a human-centric artificial intelligence (AI) based framework comprising of natural language processing, emotion modelling, unsupervised clustering methods that are collectively used to investigate the social media conversations. The investigation was carried out using 73,000 Twitter conversations related to Australia from January to September 2020. RESULTS The outcomes of this study enabled to analyse and visualise different emotion behaviours and concerns reflected on social media during the COVID-19 pandemic. First, the topic analysis showed the diverse and common concerns people have expressed during the four stages of the pandemic. It was noted that starting from personal level concerns, the concerns expressed over social media has escalated to broader concerns over time. Second, the emotion intensity and emotions state transitions showed that ‘fear’ and ‘sad’ emotions were more prominently expressed at first, however, they transition into ‘anger’ and ‘disgust’ over time. Negative emotions except ‘sad’ were significantly higher (P < .05) in the second lockdown showing increased frustration. Emotion state changes during these stages were visualised to comprehend the change in emotions over time. Third, based on the concerns expressed social media users were categorized into profiles. The profiles in the first lockdown differed from the profiles in the second lockdown showing the shift of concerns as the pandemic progressed. CONCLUSIONS This study showed diverse emotion behaviours and concerns recorded on social media during the COVID-19 pandemic. While this study establishes the use of social media to discover informed insights during a time where physical communication is impossible the outcomes also contribute towards post-pandemic recovery and understanding people’s emotions better during crises. The study exploits AI and social media to enhance our understanding of human behaviours in global emergencies, leading to improved planning and policymaking for future crises.

scholarly journals Cognitive network science as a framework for detecting structural patterns and emotions in suicide letters

2021 ◽  
Author(s):  
Massimo Stella ◽  
Trevor Swanson ◽  
Ying Li ◽  
Thomas Hills ◽  
Sofia A. Teixeira
Keyword(s):  
Network Science ◽  
Balance Theory ◽  
Main Body ◽  
Emotional States ◽  
Healthy Individuals ◽  
Cognitive Network ◽  
Suicide Notes ◽  
Negative Context ◽  
Entropy Measures ◽  
Complex Relationships

Communicating one's mindset means transmitting complex relationships between concepts and emotions. Using cognitive network science, we reconstruct the mindset around suicide as communicated in 139 genuine suicide notes. Despite their negative context, suicide notes are surprisingly positively valenced and their ending statements are markedly more emotional, i.e. elicit deeper fear/sadness but also stronger joy/trust and anticipation, than their main body. By using emotional states from the Emotional Recall Task, we "open the lid" of suicidal narratives and compare their emotional backbone against emotion recall in mentally healthy individuals. Supported by psychological literature, we introduce emotional complexity as an affective analogue of structural balance theory, measuring how elementary cycles (closed triads) of emotion co-occurrences mix positive, negative and neutral states in narratives and recollections. Both authors of suicide notes and healthy individuals exhibit less complexity and more emotional coherence than expected by chance. However, suicide narratives display higher complexity, i.e. a lower level of coherently valenced triads, than healthy individuals recalling the same states. Entropy measures identified a similar tendency for suicide letters to shift more frequently between contrasting emotional states. Our results demonstrate that suicide notes possess highly contrastive narratives of emotions, more complex than expected by null models and healthy populations.

scholarly journals Trends in Perception of COVID-19 in Polish Internet

2020 ◽  
Author(s):  
Andrzej Jarynowski ◽  
Monika Wójta-Kempa ◽  
Vitaly Belik
Keyword(s):  
Social Media ◽  
Language Processing ◽  
News Media ◽  
Public Perception ◽  
Public Information ◽  
Mitigation Strategies ◽  
The Internet ◽  
Information Campaigns ◽  
Social Distancing ◽  
Polish Government

ABSTRACTINTRODUCTIONDue to the spread of SARS CoV-2 virus responsible for COVID-19 disease, there is an urgent need to analyse COVID-2019 epidemic perception in Poland. This study aims to investigate social perception of coronavirus in the Internet media during the epidemic. It is a signal report highlighting the main issues in public perception and medical commutation in real time.METHODSWe study the perception of COVID-2019 epidemic in Polish society using quantitative analysis of its digital footprints on the Internet on platforms: Google, Twitter, YouTube, Wikipedia and electronic media represented by Event Registry, from January 2020 to 29.04.2020 (before and after official introduction to Poland on 04.03.20). We present trend analysis with a support of natural language processing techniques.RESULTSWe identified seven temporal major clusters of interest on the topic COVID-2019: 1) Chinese, 2) Italian, 3) Waiting, 4) Mitigations, 5) Social distancing and Lockdown, 6) Anti-crisis shield, 7) Restrictions releasing. There was an exponential increase of interest when the Polish government “declared war against disease” around 11/12.03.20 with a massive mitigation program. Later on, there was a decay in interest with additional phases related to social distancing and an anti-crisis legislation act with local peaks. We have found that declarations of mitigation strategies by the Polish prime minister or the minister of health gathered the highest attention of Internet users. So enacted or in force events do not affect interest to such extent. Traditional news agencies were ahead of social media (mainly Twitter) in dissemination of information. We have observed very weak or even negative correlations between a colloquial searching term ‘antiviral mask’ in Google, encyclopaedic definition in Wikipedia “SARS-CoV-2” as well official incidence series, implying different mechanisms governing the search for knowledge, panic related behaviour and actual risk of acquiring infection.CONCLUSIONSTraditional and social media do not only reflect reality, but also create it. Risk perception in Poland is unrelated to actual physical risk of acquiring COVID-19. As traditional media are ahead of social media in time, we advise to choose traditional news media for a quick dissemination of information, however for a greater impact, social media should be used. Otherwise public information campaigns might have less impact on society than expected.

scholarly journals A Data Analytic Approach in the Thematic Classification of the Reasons and Perspectives of Adolescents’ Social Media Engagement

2021 ◽  
Vol 10 (3) ◽  
pp. 1765-1770
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Learning Algorithm ◽  
Wide Spectrum ◽  
Analytic Approach ◽  
Support Vector ◽  
Social Media Engagement ◽  
Social Media Platforms ◽  
Smart Mobile ◽  
Data Analytic

Social media is one of the leading platforms where people and organizations meet. With the technological advancements in the world wide web, smart mobile devices and Internet connectivity, an increase in social media engagement is highly observable. People of all ages, one way or the other, has a social media account and their perspective, reasons, and content-preferences vary. In this study, the experience of social media engagement from the perspectiveof young people is analyzed and thematically classified using a data analytic approach which focuses on natural language processing (NLP).Results show that the social media engagement experience of the respondents reflects what social media is tothem and for themexpressed by their reasons and perspectives, respectively. The reasons of their engagement are basically connected to the contents and features of social media platforms that suit their purpose, intention, and goals of engagementexpressed by textual response and analyzed by a learning algorithm that fits multiclass models for support vector machines(SVM). The profound social media engagement of the youth leads to a wide spectrum of responses and behaviors that would affect their mental healthas defined by their reasons and perspectives

scholarly journals A Systematic Study on Sentiment Analysis based on Text Mining and Deep learning for Predictions in Stock Market Trends Through Social and News Media Data

2021 ◽  
Vol 9 (10) ◽  
pp. 1589-1593
Author(s):  
Suvigya Jain
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Stock Market ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
News Media ◽  
Market Sentiment ◽  
Market Trends

Abstract: Stock Market has always been one of the most active fields of research, many companies and organizations have focused their research in trying to find better ways to predict market trends. The stock market has been the instrument to measure the performance of a company and many have tried to develop methods that reduce risk for the investors. Since, the implementation of concepts like Deep Learning and Natural Language Processing has been made possible due to modern computing there has been a revolution in forecasting market trends. Also, the democratization of knowledge related to companies made possible due to the internet has provided the stake holders a means to learn about assets they choose to invest in through news media and social media also stock trading has become easier due to apps like robin hood etc. Every company now a days has some kind of social media presence or is usually reported by news media. This presence can lead to the growth of the companies by creating positive sentiment and also many losses by creating negative sentiments due to some public events. Our goal in this paper is to study the influence of news media and social media on market trends using sentiment analysis. Keywords: Deep Learning, Natural Language Processing, Stock Market, Sentiment analysis

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