%0 Conference Paper %1 hopcroft2011follow %A Hopcroft, John %A Lou, Tiancheng %A Tang, Jie %B Proceedings of the 20th ACM International Conference on Information and Knowledge Management %C New York, NY, USA %D 2011 %I ACM %K classification follow network prediction reciprocity relation twitter %P 1137--1146 %R 10.1145/2063576.2063740 %T Who Will Follow You Back?: Reciprocal Relationship Prediction %U http://doi.acm.org/10.1145/2063576.2063740 %X We study the extent to which the formation of a two-way relationship can be predicted in a dynamic social network. A two-way (called reciprocal) relationship, usually developed from a one-way (parasocial) relationship, represents a more trustful relationship between people. Understanding the formation of two-way relationships can provide us insights into the micro-level dynamics of the social network, such as what is the underlying community structure and how users influence each other. Employing Twitter as a source for our experimental data, we propose a learning framework to formulate the problem of reciprocal relationship prediction into a graphical model. The framework incorporates social theories into a machine learning model. We demonstrate that it is possible to accurately infer 90% of reciprocal relationships in a dynamic network. Our study provides strong evidence of the existence of the structural balance among reciprocal relationships. In addition, we have some interesting findings, e.g., the likelihood of two "elite" users creating a reciprocal relationships is nearly 8 times higher than the likelihood of two ordinary users. More importantly, our findings have potential implications such as how social structures can be inferred from individuals' behaviors. %@ 978-1-4503-0717-8

@inproceedings{hopcroft2011follow, abstract = {We study the extent to which the formation of a two-way relationship can be predicted in a dynamic social network. A two-way (called reciprocal) relationship, usually developed from a one-way (parasocial) relationship, represents a more trustful relationship between people. Understanding the formation of two-way relationships can provide us insights into the micro-level dynamics of the social network, such as what is the underlying community structure and how users influence each other. Employing Twitter as a source for our experimental data, we propose a learning framework to formulate the problem of reciprocal relationship prediction into a graphical model. The framework incorporates social theories into a machine learning model. We demonstrate that it is possible to accurately infer 90% of reciprocal relationships in a dynamic network. Our study provides strong evidence of the existence of the structural balance among reciprocal relationships. In addition, we have some interesting findings, e.g., the likelihood of two "elite" users creating a reciprocal relationships is nearly 8 times higher than the likelihood of two ordinary users. More importantly, our findings have potential implications such as how social structures can be inferred from individuals' behaviors.}, acmid = {2063740}, added-at = {2014-04-29T09:41:11.000+0200}, address = {New York, NY, USA}, author = {Hopcroft, John and Lou, Tiancheng and Tang, Jie}, biburl = {https://www.bibsonomy.org/bibtex/2641cfc3f50e787c40a829fe3910b7419/jaeschke}, booktitle = {Proceedings of the 20th ACM International Conference on Information and Knowledge Management}, description = {Who will follow you back?}, doi = {10.1145/2063576.2063740}, interhash = {22c30352126f29096d72c6941b002c16}, intrahash = {641cfc3f50e787c40a829fe3910b7419}, isbn = {978-1-4503-0717-8}, keywords = {classification follow network prediction reciprocity relation twitter}, location = {Glasgow, Scotland, UK}, numpages = {10}, pages = {1137--1146}, publisher = {ACM}, series = {CIKM '11}, timestamp = {2014-07-28T15:57:31.000+0200}, title = {Who Will Follow You Back?: Reciprocal Relationship Prediction}, url = {http://doi.acm.org/10.1145/2063576.2063740}, year = 2011 }