Twitter power: Tweets as electronic word of mouth

By Bernard Jim Jansen, Mimi Zhang, Kate Sobel, Abdur Chowdhury. J. Assoc. Inf. Sci. Technol., 2009

2,000 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/fa74b8bc0543e9d1a801cbdc25e913e0e7cfe987

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Citations in the corpus

Who is tweeting on Twitter: human, bot, or cyborg? (2010, background): “In addition to network-related studies, several previous works focus on sociotechnological aspects of Twitter [21, 23, 32, 35, 44], such as its use in the workplace or during major disaster events.”
Earthquake shakes Twitter users: real-time event detection by social sensors (2010, background): “analyzed more than 150 thousand tweets, particularly those mentioning brands in corporate accounts [12].”
Recommender Systems (2012, background): “They are found in a wide range of systems, including product reviews [228,229], e-mails [230], blogs [231] and microblogs [232].”
Enhanced Sentiment Learning Using Twitter Hashtags and Smileys (2010, methodology): “Jansen et al. (2009) used a commercial sentiment analyzer as well as a manually labeled corpus.”
Truthy: mapping the spread of astroturf in microblog streams (2010, background): “Content has been further analyzed to study consumer reactions to specific brands [28], the use of tags to alter content [25], its relation to headline news [30], and on the factors that influence the probability of a meme to be retweeted [45].”
Detecting and Tracking Political Abuse in Social Media (2011, background): “Content has been further analyzed to study consumer reactions to specific brands (Jansen et al. 2009), the use of tags to alter content (Huang, Thornton, and Efthimiadis 2010), its relation to headline news (Kwak et al.”
Exploring Social Media Engagement Behaviors in the Context of Luxury Brands (2018, result): “Earlier research on Twitter as a dominant informational and news platform supports our results, showing that the majority of brand-focused Twitter content consists of product information and consumer opinions (Jansen et al. 2009).”
Sentiment in Twitter events (2011, background): “The direct line provided by Twitter between customer opinions and businesses has potentially valuable implications for marketing as a competitive intelligence source (Jansen et al., 2009).”
Dynamical classes of collective attention in twitter (2011, background): “…including its network topology [18, 19, 20], the relations and types of messages between users [21, 22], the internal information propagation [23, 24, 25], the credibility of information [26, 27], and even its potential as an indicator of the state of mind of a population [28, 29, 30, 31, 32, 25].”
Emoticon Smoothed Language Models for Twitter Sentiment Analysis (2012, methodology): “These methods are mainly fully supervised (Jansen et al. 2009; Bermingham and Smeaton 2010) which have been introduced in the Intro-duction section.”
“This Post is Sponsored” Effects of Sponsorship Disclosure on Persuasion Knowledge and Electronic Word of Mouth in the Context of Facebook (2017, background): “Thesemotivations may include commercial and non-commercial motivations such as the desire to share a genuine liking for a product (Jansen et al. 2009; Wood and Burkhalter 2014).”
Predicting the Future with Social Media (2010, background): “Moreover, gathering information on how people converse regarding particular products can be helpful when designing marketing and advertising campaigns [1], [3].”
Social big data: Recent achievements and new challenges (2015, methodology): “[94] Twitter as eWOM advertising mechanism Sentiment analysis”
Social spending: Managing the social media mix (2011, background): “Microblogs (e.g., Jansen, Zhang, Sobel, & Chowdury, 2009) such as Twitter–— by far, the dominant platform of this type–—are structured and used to share relatively shallow information with a relatively short half-life.”
#Snowden: Understanding Biases Introduced by Behavioral Differences of Opinion Groups on Social Media (2016, background): “Many attempted to predict election results (see review in [24]), public polls [37], stock market [7], and consumer opinions [27, 44].”
Sentiment analysis of Twitter data for predicting stock market movements (2016, background): “This situation makes Twitter like a corpus with valuable data for researchers [4].”