Should We Use the Sample? Analyzing Datasets Sampled from Twitter’s Stream API

Wang, Yazhe; Callan, Jamie; Zheng, Baihua

doi:10.1145/2746366

Cited by 63 publications

(49 citation statements)

References 28 publications

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“…This also avoids overestimating the importance of infrequent items [61]. We motivate the choice of thresholds for the odds ratio computation in Section 3.4.…”

Section: Methodsmentioning

confidence: 99%

What do computer scientists tweet? Analyzing the link-sharing practice on Twitter

Schmitt

Jäschke

2017

PLoS ONE

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Twitter communication has permeated every sphere of society. To highlight and share small pieces of information with possibly vast audiences or small circles of the interested has some value in almost any aspect of social life. But what is the value exactly for a scientific field? We perform a comprehensive study of computer scientists using Twitter and their tweeting behavior concerning the sharing of web links. Discerning the domains, hosts and individual web pages being tweeted and the differences between computer scientists and a Twitter sample enables us to look in depth at the Twitter-based information sharing practices of a scientific community. Additionally, we aim at providing a deeper understanding of the role and impact of altmetrics in computer science and give a glance at the publications mentioned on Twitter that are most relevant for the computer science community. Our results show a link sharing culture that concentrates more heavily on public and professional quality information than the Twitter sample does. The results also show a broad variety in linked sources and especially in linked publications with some publications clearly related to community-specific interests of computer scientists, while others with a strong relation to attention mechanisms in social media. This refers to the observation that Twitter is a hybrid form of social media between an information service and a social network service. Overall the computer scientists’ style of usage seems to be more on the information-oriented side and to some degree also on professional usage. Therefore, altmetrics are of considerable use in analyzing computer science.

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“…This also avoids overestimating the importance of infrequent items [61]. We motivate the choice of thresholds for the odds ratio computation in Section 3.4.…”

Section: Methodsmentioning

confidence: 99%

What do computer scientists tweet? Analyzing the link-sharing practice on Twitter

Schmitt

Jäschke

2017

PLoS ONE

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“…To collect tweets, we created a Twitter app account to gain access to the limited API data Twitter makes freely accessible to all registered app users (Twitter Developer, n.d.). We were able to conduct specific searches for tweets incorporating the #deleteuber hashtag, following similar procedures from extant research utilizing social media as data (Boyd & Crawford, 2012;Chae, 2015;Giglietto & Selva, 2014;Humphreys, Gill, Krishnamurthy, & Newbury, 2013;Kim, Heo, Choi, & Park, 2014;Wang, Callan, & Zheng, 2015).…”

Section: Data Collectionmentioning

confidence: 99%

Blurred (Identity) Lines: A Content Analysis of the #deleteuber Crisis on Twitter

Miller

Kendall

2018

JICRCR

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Social media have established a growing prevalence and influence in social change, in political movements, and as vehicles for messages related to crisis. The movement #deleteuber demonstrated this growing trend. Using quantitative content analysis, 2,000 tweets posted on Twitter were analyzed in the 2 weeks following the incident to measure how media framing may impact organizational identity. Findings reveal that users on Twitter largely framed the crisis as political, opinionated, and episodic in nature. Additionally, users most commonly associated the crisis with the organization as a collective rather than with the CEO as an individual responsible for actions prompting the crisis, thus blurring the demarcation between personal and organizational identity in online spaces.

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“…However, Twitter states a sample size range between 1% and 10% for tweets. Studies that measured this sample size reported a sample size between 0.95% and 9.6% for tweets and between 10% and 45% for users [4,5]. Wang et.…”

Section: Technical Recording Limitations For the Analysis To Be Consimentioning

confidence: 99%

“…concluded that "the sample datasets truthfully reflect the daily and hourly activity patterns of the Twitter users. (...) Even with a very small sampling ratio (i.e., 0.95%), the sample datasets (...) preserve the relative importance (i.e., frequency of appearance) of the content terms" [5].…”

Section: Technical Recording Limitations For the Analysis To Be Consimentioning

confidence: 99%

“…At least for large datasets [4], these samples "truthfully reflect the daily and hourly activity patterns of the Twitter users (...) and preserve the relative importance (...) of content terms" [5]. Twitter might not be the biggest, but one of the most influencing social networks providing a microblogging service with more than 320 million active Twitter users in 2017.…”

Section: Introductionmentioning

confidence: 99%

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The #BTW17 Twitter Dataset–Recorded Tweets of the Federal Election Campaigns of 2017 for the 19th German Bundestag

Kratzke

2017

Data

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Abstract:The German Bundestag elections are the most important elections in Germany. This dataset comprises Twitter interactions related to German politicians of the most important political parties over several months in the (pre-)phase of the German federal election campaigns in 2017. The Twitter accounts of more than 360 politicians were followed for four months. The collected data comprise a sample of approximately 10 GB of Twitter raw data, and they cover more than 120,000 active Twitter users and more than 1,200,000 recorded tweets. Even without sophisticated data analysis techniques, it was possible to deduce a likely political party proximity for more than half of these accounts simply by looking at the re-tweet behavior. This might be of interest for innovative data-driven party campaign strategists in the future. Furthermore, it is observable, that, in Germany, supporters and politicians of populist parties make use of Twitter much more intensively and aggressively than supporters of other parties. Furthermore, established left-wing parties seem to be more active on Twitter than established conservative parties. The dataset can be used to study how political parties, their followers and supporters make use of social media channels in political election campaigns and what kind of content is shared.

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Should We Use the Sample? Analyzing Datasets Sampled from Twitter’s Stream API

Cited by 63 publications

References 28 publications

What do computer scientists tweet? Analyzing the link-sharing practice on Twitter

What do computer scientists tweet? Analyzing the link-sharing practice on Twitter

Blurred (Identity) Lines: A Content Analysis of the #deleteuber Crisis on Twitter

The #BTW17 Twitter Dataset–Recorded Tweets of the Federal Election Campaigns of 2017 for the 19th German Bundestag

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