Towards more systematic Twitter analysis: metrics for tweeting activities
By Axel Bruns, Stefan Stieglitz. 2013
339 citations recorded by the source corpus.
Subfields: Communication, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/2c0aea7177a909c4bc9990ddfa0a37c5d2d110d6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Communication
- Stefan Stieglitz
- M. Strohmaier
- Q. Liao
- Wai-Tat Fu
- David J. Stillwell
- Michal Kosinski
- K. Cate
- Sean C. Rife
In Statistical and Nonlinear Physics
- Axel Bruns
- Stefan Stieglitz
- Christoph Neuberger
- Michal Kosinski
- Q. Liao
- M. Strohmaier
- Wai-Tat Fu
- David J. Stillwell
Citations in the corpus
- #Snowden: Understanding Biases Introduced by Behavioral Differences of Opinion Groups on Social Media (2016, methodology): “[10, 11] suggest that using two metrics—the percentage of original tweets containing URL, and the percentage of retweets among all tweets — one can classify if the discussions focus more on information sharing (high URL and retweets percentage), or more on making original commentary (low URL and ret”
- An exploratory study of COVID-19 misinformation on Twitter (2020, neutral): “Hashtags are brief keywords or abbreviations prefixed by a # that are used on social media platforms to make tweets more easily searchable [10].”
- Participant recruitment and data collection through Facebook: the role of personality factors1 (2016, methodology): “Combined with new, innovated methods of collecting and analyzing existing data (such as those used to examine other types of social media; e.g. Bruns & Stieglitz, 2013; Procter, Farida, & Voss, 2013), Facebook appears to be a promising new space for social scientists.”
- Social Media Analytics (2014, background): “This recent interest in “Big Social Data” (Manovich 2012; Burgess and Bruns 2012) has been driven in part by facilitated access to large-scale empirical datasets from popular online social networking platforms such as Twitter, Facebook, and LinkedIn, as well as from other platforms that facilitate m”
- Twitter data: What do they represent? (2014, background): “We found the #auspol hashtag (for Australian politics) to be dominated by a very small number of extremely active users, for example [8].”