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The Sage Handbook of Social Media Research Methods

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The article was published on 2017-01-27 and is currently open access. It has received 74 citations till now. The article focuses on the topics: Social media.

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Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter

TL;DR: In this article, the authors used machine learning techniques to analyze about 1.9 million tweets related to COVID-19 collected from January 23 to March 7, 2020 and found that fear for the unknown nature of the coronavirus is dominant in all topics.
Journal ArticleDOI

Social Media data: Challenges, opportunities and limitations in urban studies

TL;DR: A comprehensive and descriptive framework for the study of urban phenomena through LBSN data is the main contribution of this study.
Journal ArticleDOI

Social media, internet use and suicide attempts in adolescents

TL;DR: There is an independent association between problematic use of social media/internet and suicide attempts in young people, however, the direction of causality, if any, remains unclear.

The Routledge Companion to Media and Tourism

TL;DR: The Routledge Companion to Media and Tourism as mentioned in this paper provides a comprehensive overview of the research into the convergence of media and tourism and specifically investigates the concept of mediatized tourism, focusing on the ways in which different forms of media content and consumption converge and the consequential effects on tourism and tourists.
Journal ArticleDOI

Innovation under pressure: Implications for data privacy during the Covid-19 pandemic

TL;DR: This article presents two critical cases on digital surveillance technologies implemented during the Covid-19 pandemic and delineates the privacy implications thereof and raises the question of whether there is a way to have expedited privacy assessments that could anticipate and help mitigate adverse privacy implications these may have on society.
References
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Journal ArticleDOI

Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter

TL;DR: In this article, the authors used machine learning techniques to analyze about 1.9 million tweets related to COVID-19 collected from January 23 to March 7, 2020 and found that fear for the unknown nature of the coronavirus is dominant in all topics.
Journal ArticleDOI

Social Media data: Challenges, opportunities and limitations in urban studies

TL;DR: A comprehensive and descriptive framework for the study of urban phenomena through LBSN data is the main contribution of this study.
Journal ArticleDOI

Social media, internet use and suicide attempts in adolescents

TL;DR: There is an independent association between problematic use of social media/internet and suicide attempts in young people, however, the direction of causality, if any, remains unclear.

The Routledge Companion to Media and Tourism

TL;DR: The Routledge Companion to Media and Tourism as mentioned in this paper provides a comprehensive overview of the research into the convergence of media and tourism and specifically investigates the concept of mediatized tourism, focusing on the ways in which different forms of media content and consumption converge and the consequential effects on tourism and tourists.
Journal ArticleDOI

Innovation under pressure: Implications for data privacy during the Covid-19 pandemic

TL;DR: This article presents two critical cases on digital surveillance technologies implemented during the Covid-19 pandemic and delineates the privacy implications thereof and raises the question of whether there is a way to have expedited privacy assessments that could anticipate and help mitigate adverse privacy implications these may have on society.