Use of social media data for disease based social network analysis and network modeling: A Systematic Review

Ramamoorthy, Thilagavathi; Karmegam, Dhivya; Mappillairaju, Bagavandas

doi:10.1080/17538157.2021.1905642

Cited by 8 publications

(8 citation statements)

References 42 publications

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“…We have found a big gap between receiving and offering support in the DSN, which confirms Hanneman and Riddle (2005) that SNS (e.g., Facebook, Twitter) tend to show more homogeneity regarding out-degree than in-degree. In the same vein, some studies have suggested that users would restrict online support to a limited number of users (Lin and Li, 2021;Lu et al, 2021;Ramamoorthy et al, 2021). The mechanism of low out-degree and its effects on SNS should be investigated in future research.…”

Section: A B Figurementioning

confidence: 99%

“…In contrast to its potential with relational data, few SNA studies have looked into users’ content in online communities about diseases or the characteristics of users’ networks ( Ramamoorthy et al, 2021 ). Some of the limited studies have examined social support on social network sites.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…Given the omnipresence of SNS and their dual effects on well-being, a key research question with practical and theoretical importance is to investigate the various conditions by which SNS may either enhance or interfere with well-being ( Ramamoorthy et al, 2021 ; Valkenburg et al, 2021 ; Hall and Liu, 2022 ; Parry et al, 2022 ). It is to understand why, how, and/or for whom SNS use is associated with well-being ( Valkenburg, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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Online health community for change: Analysis of self-disclosure and social networks of users with depression

Khoo

2023

Front. Psychol.

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BackgroundA key research question with theoretical and practical implications is to investigate the various conditions by which social network sites (SNS) may either enhance or interfere with mental well-being, given the omnipresence of SNS and their dual effects on well-being.Method/processWe study SNS’ effects on well-being by accounting for users’ personal (i.e., self-disclosure) and situational (i.e., social networks) attributes, using a mixed design of content analysis and social network analysis.Result/conclusionWe compare users’ within-person changes in self-disclosure and social networks in two phases (over half a year), drawing on Weibo Depression SuperTalk, an online community for depression, and find: ① Several network attributes strengthen social support, including network connectivity, global efficiency, degree centralization, hubs of communities, and reciprocal interactions. ② Users’ self-disclosure attributes reflect positive changes in mental well-being and increased attachment to the community. ③ Correlations exist between users’ topological and self-disclosure attributes. ④ A Poisson regression model extracts self-disclosure attributes that may affect users’ received social support, including the writing length, number of active days, informal words, adverbs, negative emotion words, biological process words, and first-person singular forms.InnovationWe combine social network analysis with content analysis, highlighting the need to understand SNS’ effects on well-being by accounting for users’ self-disclosure (content) and communication partners (social networks).Implication/contributionAuthentic user data helps to avoid recall bias commonly found in self-reported data. A longitudinal within-person analysis of SNS’ effects on well-being is helpful for policymakers in public health intervention, community managers for group organizations, and users in online community engagement.

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Section: A B Figurementioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online health community for change: Analysis of self-disclosure and social networks of users with depression

Khoo

2023

Front. Psychol.

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“…SA focuses on studying people's reactions, sentiments, and thoughts regarding events, products, public policies in different fields, and life in general as it is perceived over the internet in written form. SA has made important advances in applications ranging from pin-sharp targeted advertising [15], stock market predictions [16], [17], or even the identification of human emotion in text and its relation to the state of a subject's mental health [18]- [21], or other health-related applications [22]- [25]. Given the context of confinements and social distancing around which society finds itself as a result of the pandemic brought upon by the SARS-CoV-2 virus, this subject has been of special interest to mental health professionals and governments alike, as it can directly impact the well-being of entire populations [25]- [30].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics

et al. 2022

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“… 15 Twitter data provides a rich source of opinions and sentiments that can be applied in multi-domain applications like market analysis, political issues, religious views, and the health surveillance monitoring system. 16 , 17 Understanding sentiment of Twitter users talking about vaccines is convenient and useful way to explore public opinion on vaccines and vaccination. Sentiment analysis involves processing unstructured text to identify meaningful patterns and gain new insights.…”

Section: Introductionmentioning

confidence: 99%

Public attitudes on social media toward vaccination before and during the COVID-19 pandemic

Shah

Biswas

Ali

et al. 2022

Human Vaccines & Immunotherapeutics

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With the success of COVID-19 vaccines in clinical trials, vaccination programs are being administered for the population with the hopes of herd immunity. However, the success of any vaccination program depends on the percentage of people willing to get vaccination which is influenced by social, economic, demographic, and vaccine-specific factors. Thus, it is important to understand public attitudes and perceptions toward vaccination. This study aims to measure public attitude toward vaccines and vaccinations before and during the COVID-19 pandemic, using public data from Twitter. A total of 880,586 tweets for 57,529 unique users were included in the study. Most of the tweets were posted in five languages: French, English, Swedish, Dutch, and Italian. These tweets were divided into two time periods: before COVID-19 (T1) and during COVID-19 (T2). This study observed the shift in the sentiments of the public attitude toward vaccines before and during COVID-19 pandemic. Both positive and negative shifts in sentiments were observed for the users of various languages but shifts toward positive sentiments were more prominent during the COVID-19 pandemic.

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Use of social media data for disease based social network analysis and network modeling: A Systematic Review

Cited by 8 publications

References 42 publications

Online health community for change: Analysis of self-disclosure and social networks of users with depression

Online health community for change: Analysis of self-disclosure and social networks of users with depression

Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics

Public attitudes on social media toward vaccination before and during the COVID-19 pandemic

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