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DISTINGUISHING TOPICAL AND SOCIAL GROUPS BASED ON COMMON IDENTITY AND BOND THEORY

DISTINGUISHING TOPICAL AND SOCIAL GROUPS BASED ON COMMON IDENTITY AND BOND THEORY. Przemyslaw A. Grabowicz Luca M. Aiello Vìctor M. Eguìluz Alejandro Jaimes. We built a classifier that distinguishes if a given set of people is either a social or a topical group. What are social and

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DISTINGUISHING TOPICAL AND SOCIAL GROUPS BASED ON COMMON IDENTITY AND BOND THEORY

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  1. DISTINGUISHING TOPICAL AND SOCIAL GROUPS BASED ON COMMON IDENTITY AND BOND THEORY Przemyslaw A. Grabowicz Luca M. Aiello Vìctor M. Eguìluz Alejandro Jaimes

  2. We built a classifier that distinguishes if a given set of people is either a socialor a topicalgroup.

  3. What are social and topical groups?

  4. Social groups Friends www.news.com.au

  5. Characteristics of social groups • Direct reciprocityof interactions • Small talk (broad range of topics in conversations)

  6. Topical groups A camera club http://www.flickr.com/photos/59571907@N03/5545401056/

  7. Characteristics of topical groups • General reciprocity of interactions • Conversations on a narrow range of topics

  8. Two types of metrics Based on reciprocityof interactions Based on diversity of topics (Shannon’s entropy)

  9. Reciprocity metrics 1. intra-group reciprocity intra-reciprocity: -------------------------------------------------- inter-reciprocity: 2.

  10. Diversity of topics’ metrics 1. H(g)– Shannon’s entropy of terms/tags normalized by the average for all groups having the same number of terms 2.

  11. Dataset – Flickr, 2008 Tags extracted from photos: • from a group pool • commented in a group • favorited in a group

  12. Human labeling of groups Consists of exploring: • text of comments • group profiles • photos • tags • maps

  13. Results Reciprocity Normalized entropy

  14. Classifier AUC 0.75 Accuracy 0.76 1. • hg for comments 1 • tg for comments 2 • ug for comments 3 AUC 0.88 Accuracy 0.80 AUC 0.87 Accuracy 0.80 • hg for favorites 4 2. • bg for comments 5

  15. Conclusions Findings: • The metrics work as the theory predicts • Agreement and accuracy depend on value of the score • Groups found with a community detection algorithm are more social than declared groups Future work: • Entropy is a simple measure, could be replaced with something what understands text • NLP • Binary classifier has its limitations • multi-label classification

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