The quality of online social relationships
Communications of the ACM - How the virtual inspires the real
Think different: increasing online community participation using uniqueness and group dissimilarity
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Analyzing Actors and Their Discussion Topics by Semantic Social Network Analysis
IV '06 Proceedings of the conference on Information Visualization
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Preferential behavior in online groups
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Developing metrics to characterize Flickr groups
Journal of the American Society for Information Science and Technology
Group Profiling for Understanding Social Structures
ACM Transactions on Intelligent Systems and Technology (TIST)
The life and death of online groups: predicting group growth and longevity
Proceedings of the fifth ACM international conference on Web search and data mining
Friendship prediction and homophily in social media
ACM Transactions on the Web (TWEB)
Defining and evaluating network communities based on ground-truth
Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics
Deep Twitter diving: exploring topical groups in microblogs at scale
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
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Social groups play a crucial role in social media platforms because they form the basis for user participation and engagement. Groups are created explicitly by members of the community, but also form organically as members interact. Due to their importance, they have been studied widely (e.g., community detection, evolution, activity, etc.). One of the key questions for understanding how such groups evolve is whether there are different types of groups and how they differ. In Sociology, theories have been proposed to help explain how such groups form. In particular, the common identity and common bond theory states that people join groups based on identity (i.e., interest in the topics discussed) or bond attachment (i.e., social relationships). The theory has been applied qualitatively to small groups to classify them as either topical or social. We use the identity and bond theory to define a set of features to classify groups into those two categories. Using a dataset from Flickr, we extract user-defined groups and automatically-detected groups, obtained from a community detection algorithm. We discuss the process of manual labeling of groups into social or topical and present results of predicting the group label based on the defined features. We directly validate the predictions of the theory showing that the metrics are able to forecast the group type with high accuracy. In addition, we present a comparison between declared and detected groups along topicality and sociality dimensions.