How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media?

By M. de Choudhury, Yu-Ru Lin, Hari Sundaram, K. Candan, Lexing Xie, Aisling Kelliher. 2010

242 citations recorded by the source corpus.

Platforms such as Twitter have provided researchers with ample opportunities to analytically study social phenomena. There are however, significant computational challenges due to the enormous rate of production of new information: researchers are therefore, often forced to analyze a judiciously selected “sample” of the data. Like other social media phenomena, information diffusion is a social process–it is affected by user context, and topic, in addition to the graph topology. This paper studies the impact of different attribute and topology based sampling strategies on the discovery of an important social media phenomena–information diffusion.We examine several widely-adopted sampling methods that select nodes based on attribute (random, location, and activity) and topology (forest fire) as well as study the impact of attribute based seed selection on topology based sampling. Then we develop a series of metrics for evaluating the quality of the sample, based on user activity (e.g. volume, number of seeds), topological (e.g. reach, spread) and temporal characteristics (e.g. rate). We additionally correlate the diffusion volume metric with two external variables–search and news trends. Our experiments reveal that for small sample sizes (30%), a sample that incorporates both topology and user context (e.g. location, activity) can improve on naive methods by a significant margin of ~15-20%.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “In social datasets, linking biases can be further exacerbated by how data is collected and sampled, and by how links are defined, impacting the observed properties of a variety of networkbased user attributes, such as their centrality within a social network (Choudhury et al., 2010; González-Bailón ”
Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose (2013, background): “(De Choudhury et al. 2010) studies the propagation patterns of URLs on sampled Twitter data.”
Do all birds tweet the same?: characterizing twitter around the world (2011, background): “[4] examine Twitter data and study how different sampling methods can influence the level of diffusion of information.”
Searching microblogs: coping with sparsity and document quality (2011, methodology): “choudhury [6] 118,506 9,998,756 7.”
When is it biased?: assessing the representativeness of twitter's streaming API (2014, background): “In [7], the authors analyze the eﬀect data sampling has on the way link propagation is perceived.”
Bad news travel fast: a content-based analysis of interestingness on Twitter (2011, unknown): “89% CHOUDHURY-EXT [5] 277,666 29,000,000 8.”
Sentiment Knowledge Discovery in Twitter Streaming Data (2010, background): “[7] study how data sampling strategies impact the discovery of information diffusion.”
Sampling Content from Online Social Networks (2015, methodology): “…node sampling strategies (such as uniform sampling, random-walk–based sampling), have been studied to ascertain whether the subgraphs obtained by these strategies can be used to recover the properties of the complete network, including topological properties such et al. 2010] over the networks .”