Reliable online social network data collection
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Large quantities of information are shared through online social networks, making them attractive sources of data for social network research. When studying the usage of online social networks, these data may not describe properly users’ behaviours. For instance, the data collected often include content shared by the users only, or content accessible to the researchers, hence obfuscating a large amount of data that would help understanding users’ behaviours and privacy concerns. Moreover, the data collection methods employed in experiments may also have an effect on data reliability when participants self-report inacurrate information or are observed while using a simulated application. Understanding the effects of these collection methods on data reliability is paramount for the study of social networks; for understanding user behaviour; for designing socially-aware applications and services; and for mining data collected from such social networks and applications. This chapter reviews previous research which has looked at social network data collection and user behaviour in these networks. We highlight shortcomings in the methods used in these studies, and introduce our own methodology and user study based on the Experience Sampling Method; we claim our methodology leads to the collection of more reliable data by capturing both those data which are shared and not shared. We conclude with suggestions for collecting and mining data from online social networks.
Abdesslem , F B , Parris , I & Henderson , T 2012 , Reliable online social network data collection . in A Abraham (ed.) , Computational Social Networks : Mining and Visualization . Springer-Verlag , London, UK , pp. 183-210 . https://doi.org/10.1007/978-1-4471-4054-2_8
Computational Social Networks
This is an author version of this chapter. The final publication is available at www.springerlink.com
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