Is it really about me?: message content in social awareness streams

By Mor Naaman, Jeffrey Boase, Chih-Hui Lai. 2010

912 citations recorded by the source corpus.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Motivations include communication, friendship maintenance, job seeking, self-presentation or promotion [DiMicco et al. 2008; Joinson 2008; Lampe et al. 2006; Naaman et al. 2010], which are also central to understanding ethical aspects of using this data.”
Reusable workflows for gender prediction (2018, background): “Messages from the social media by personal users display a trend towards more personal content (Naaman et al., 2010), which makes it a useful dataset for learning about the users’ characteristics, related to demographics, psychological pro-ﬁle or mental health.”
OMG, I Have to Tweet that! A Study of Factors that Influence Tweet Rates (2012, background): “…events on social media does not directly reflect the true frequency of event occurrence in the real-world, but instead is a complex function of individual experience combined with what each individual believes would interest their friends or followers (Java et al. 2007; Naaman, Boase and Lai 2010).”
A framework for interpreting social media interactions in the public sector (2013, background): “…with citizens include Twitter mood indicators (Mislove, Lehmann, Ahn, Onnela, & Rosenquist, 2010), or the analysis of an agency's Twitter network composition with the help of computational social sciences methods (Lazer, Pentland, Adamic, Aral, Barabási, et al., 2009; Naaman et al., 2010).”
I Know the Feeling: Learning to Converse with Empathy (2018, background): “As examples, a recent study found that 80% of Twitter users seem to post mostly about themselves (Naaman et al., 2010), and ELIZA (Weizenbaum, 1966), one of the earliest chatbots developed, focused most of its attention on asking its conversational partners why they were feeling a certain way.”
#Earthquake: Twitter as a Distributed Sensor System (2013, background): “…the microcontroller: she collects the information that she considers remarkable and important to communicate (Sternberg 2009), in order to document one's life, provide a commentary, or to build a community (Nardi et al. 2004, Java et al. 2009, Naaman et al. 2010), and publishes this information.”
Geography of Twitter networks (2012, background): “Despite those problems, the public timeline is commonly used to sample Twitter messages (e.g., Java et al., 2007; Naaman et al., 2010; Golder & Yardi, 2010).”
Characterizing debate performance via aggregated twitter sentiment (2010, background): “[9] coded a sample of tweets to broadly classify users as self-broadcasters or informers.”
What's Different about Social Media Networks? A Framework and Research Agenda (2014, background): “People often obtain content from content streams automatically filtered from the network by the platform (Naaman et al. 2010).”
Language Differences and Metadata Features on Twitter (2010, background): “A few studies have studied content on Twitter through systematic analysis of messages [1, 5].”
Twitter for crisis communication: lessons learned from Japan's tsunami disaster (2011, background): “Naaman et al. (2010) also emphasised the information sharing aspect of Twitter noting that the majority of Twitter users tend to tweet about themselves (‘meformers’ versus ‘informers’).”
You are what you tweet: Personality expression and perception on Twitter (2012, background): “…to describe their daily routines, carry out conversations , report news, and share information (e.g., Java, Song, Finin, & Tseng, 2007; Naaman, Boase, & Lai, 2010), producing a textbased ''social awareness stream'' consisting of everyday thoughts, feelings, and conversations (Naaman et al., 2010).”
Major life changes and behavioral markers in social media: case of childbirth (2013, background): “ties, and at the same time building new ties [22].”
Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media (2015, background): “With the wide-spread adoption of social networking services over the last 10-15 years, much research has focused on understanding people’s motivations and participatory behavior on these sites, both from a qualitative as well as quantitative perspective [27, 14, 19, 21, 13, 32, 26, 36, 6, 42].”
Information credibility on twitter (2011, background): “While most messages on Twitter are conversation and chatter, people also use it to share relevant information and to report news [13, 22, 21].”
Twitinfo: aggregating and visualizing microblogs for event exploration (2011, background): “detailed a division between informers, who spread information, and meformers, who discuss their own affairs [19].”
Tweets are forever: a large-scale quantitative analysis of deleted tweets (2013, background): “Such efforts have involved manual coding of small samples [17], utilizing the crowd [2] and automatic topic modeling for large samples [19].”
Earthquake shakes Twitter users: real-time event detection by social sensors (2010, background): “analyzed contents of messages from more than 350 Twitter users and manually classified messages into nine categories [19].”