Abstract
Socially cohesive groups tend to share similar ideas and express themselves in similar ways when posting their thoughts in online social networks. Therefore, some researchers have conducted studies to uncover the issues discussed by groups who are structurally connected in a network. In this study, we take advantage of the language usage patterns present in online communication to unveil affinity groups, i.e. like-minded people, who are not necessarily interacting in the network currently. We analyze 735K tweets written by 620 unique users and compute scores for 14 grammatical categories using the linguistic inquiry word count software (LIWC). With the LIWC scores, we build a vector for each user, apply a similarity measure and feed an affinity propagation clustering algorithm to find the affinity groups. Following the proposed method, clusters of religious activists, journalists, entrepreneurs, among others emerge. We automatically characterize each cluster using a topic modeling algorithm and validate the generated topics with a user study conducted with 200 people. As a result, more than 70% of the participants agreed on their selection. These results confirm that communities share certain similarities in the use of language, traits that characterize their behavior and grouping.
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A Appendix
A Appendix
1.1 A.1 Survey Example
According to your opinion, which of the following topics best describe the set of words presented below. Please, choose only one or two topics. Underline the words that justify your selection.
1.2 A.2 Participants’ Demographics
From the surveyed people, we obtained the demographics described in the following Table 2:
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Mendieta, J., Baquerizo, G., Villavicencio, M., Vaca, C. (2017). Affinity Groups: A Linguistic Analysis for Social Network Groups Identification. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10540. Springer, Cham. https://doi.org/10.1007/978-3-319-67256-4_21
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DOI: https://doi.org/10.1007/978-3-319-67256-4_21
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