Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior

By Stefan Stieglitz, Linh Dang-Xuan. Journal of Management Information Systems, 2013

1,497 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/f77e4dfe7771bf3e02e1b676aec7dddf3fc94cdb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Sociology and Political Science

In Statistical and Nonlinear Physics

Citations in the corpus

Using social media to enhance citizen engagement with local government: Twitter or Facebook? (2018, background): “, 2017; Mergel, 2013a), and in particular into the analysis of mood and feelings (Dardenne et al., 2011; Stieglitz and Dang-Xuan, 2013), the following hypotheses are proposed:”
Social Media Analytics (2014, methodology): “…ideal public platform for disseminating political information and opinions quickly and widely Business & Information Systems Engineering 2|2014 89 (Stieglitz and Dang-Xuan 2013a, 2013b; Bruns and Highfield 2013), political actors (e.g., politicians, parties, foundations, etc.) have also begun to…”
Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics (2014, positive): “The software is free for academic research and has been tested and validated in previous research (Garcia and Schweitzer 2011; Gruzd, Doiron and Mai 2011; Stieglitz and Dang-Xuan 2013; Thelwall and Buckley 2013; Thelwall, Buckley and Paltoglou 2012; Thelwall et al. 2010).”
To fear or not to fear? Applying the social amplification of risk framework on two environmental health risks in Singapore (2018, background): “Social media platforms such as Facebook and Twitter, which are quickly becoming dominant gateways to the Internet, also serve as a unique platform for risk amplification (Stieglitz and Dang-Xuan 2013).”
The MAD Model of Moral Contagion: The Role of Motivation, Attention, and Design in the Spread of Moralized Content Online (2019, neutral): “…2015; Kramer, Guillory, & Hancock, 2014), Twitter (Hansen, Arvidsson, Nielsen, Colleoni, & Etter, 2011; Quercia, Ellis, Capra, & Crowcroft, 2011; Stieglitz & Dang-Xuan, 2013), Google + (Heimbach et al., 2015; Hochreiter & Waldhauser, 2014), and Weibo (a popular Chinese microblogging platform;…”
Twitter data: What do they represent? (2014, background): “But given that researchers as well as popular media are already positioning Twitter and other social media platforms as a window on society itself [2, 31], it is important to begin to address such questions: even where no comprehensive perspective of Twitter activities exists, more specific Twitter ”
An exploratory study of COVID-19 misinformation on Twitter (2020, neutral): “The diffusion of misinformation tweets can be analysed in terms of likes and retweet [76].”
Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak (2020, background): “Media is serving as a paradigm for communication across different networks (Stieglitz and Dang-Xuan, 2013), it is thus important to understand the interaction between information and emotional wellbeing.”
Teens’ Motivations to Spread Fake News on WhatsApp (2020, background): “This is where the emphasis should be placed, as negative information spreads much more quickly than positive information (Stieglitz & Dang-Xuan, 2013, cit. in Yin et al., 2019).”
Anger, Fear, and Echo Chambers: The Emotional Basis for Online Behavior (2019, background): “In relation to sharing of content, Stieglitz and Dang-Xuan (2013), Bail (2016), and Brady, Wills, Jost, Tucker, & Van Bavel (2017) also found that emotionally charged messages on social media are more likely to be shared than neutral messages.”
Measuring Emotional Contagion in Social Media (2015, methodology): “ exists a variety of sentiment analysis algorithms able to capture positive and negative sentiment, some speciﬁcally designed for short, informal texts [2, 36, 25]. In this work, we use SentiStrength [44, 43, 41] to annotate the tweets with positive and negative sentiment scores. Compared with other”
Advances in Social Media Research: Past, Present and Future (2017, background): “Stieglitz and Dang-Xuan (2013) examine the relationship between user behaviour and sentiment to conclude that emotional Twitter messages have a higher retweet tendency.”