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Assessing the Coverage of Data Collection Campaigns on Twitter: A Case Study

Vassilis Plachouras, Yannis Stavrakas, and Athanasios Andreou

Institute for the Management of Information Systems (IMIS), ATHENA Research Center, Artemidos 6 & Epidavrou, Maroussi 15125, Athens, Greece
vplachouras@imis.athena-innovation.gr
yannis@imis.athena-innovation.gr
athan.andreou@gmail.com

Abstract. Online social networks provide a unique opportunity to access and analyze the reactions of people as real-world events unfold. The quality of any analysis task, however, depends on the appropriateness and quality of the collected data. Hence, given the spontaneous nature of user-generated content, as well as the high speed and large volume of data, it is important to carefully define a data-collection campaign about a topic or an event, in order to maximize its coverage (recall). Motivated by the development of a social-network data management platform, in this work we evaluate the coverage of data collection campaigns on Twitter. Using an adaptive language model, we estimate the coverage of a campaign with respect to the total number of relevant tweets. Our findings support the development of adaptive methods to account for unexpected real-world developments, and hence, to increase the recall of the data collection processes.

Keywords: Social networks, data management, event tracking

LNCS 8186, p. 598 ff.

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