OMG, I Have to Tweet that! A Study of Factors that Influence Tweet Rates

By Emre Kıcıman. 2012

40 citations recorded by the source corpus.

Many studies have shown that social data such as tweets are a rich source of information about the real-world including, for example, insights into health trends. A key limitation when analyzing Twitter data, however, is that it depends on people self-reporting their own behaviors and observations. In this paper, we present a large-scale quantitative analysis of some of the factors that influence self-reporting bias. In our study, we compare a year of tweets about weather events to ground-truth knowledge about actual weather occurrences. For each weather event we calculate how extreme, how expected, and how big a change the event represents. We calculate the extent to which these factors can explain the daily variations in tweet rates about weather events. We find that we can build global models that take into account basic weather information, together with extremeness, expectation and change calculations to account for over 40% of the variability in tweet rates. We build location-specific (i.e., a model per each metropolitan area) models that account for an average of 70% of the variability in tweet rates.

Subfields: Epidemiology, Statistical and Nonlinear Physics, Transportation

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Citations in the corpus

Discussion Graphs: Putting Social Media Analysis in Context (2014, background): “The most similar research effort in motivation includes Heer et al. (Heer and Perer 2011)’s Orion proposal for an interactive network modeling and visualization tool, enabling rapid manipulation of large graphs using relational operators and network analytics.”
Sentiment analysis on evolving social streams: how self-report imbalances can help (2014, background): “Extreme-average sentiment report imbalance: People tend to express extreme feelings more than average feelings in social environments [2, 11, 10, 28].”
On Unravelling Opinions of Issue Specific-Silent Users in Social Media (2016, background): “However, social media content is generated when users choose to self-report their thoughts (Kiciman 2012; Guerra, Meira, and Cardie 2014).”
Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media (2017, background): “This ignores population and various selection biases [47, 52].”
A Critical Review of Online Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2018, background): “We will review both recent work raising concerns about what can be legitimately inferred from existing social datasets [31, 35], as well as research that tries to quantify the limits of such data [10, 14, 19, 27].”
Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media (2015, unknown): “to population and self-reporting biases [30, 12, 22].”