Trends Prediction Using Social Diffusion Models
The importance of the ability to predict trends in social media has been growing rapidly in the past few years with the growing dominance of social media in our everyday’s life. Whereas many works focus on the detection of anomalies in networks, there exist little theoretical work on the prediction...
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| Format: | Article |
| Language: | en_US |
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Springer-Verlag
2013
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| Online Access: | http://hdl.handle.net/1721.1/79923 https://orcid.org/0000-0002-8053-9983 https://orcid.org/0000-0002-3410-9587 |
| _version_ | 1826216255414075392 |
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| author | Altshuler, Yaniv Pan, Wei Pentland, Alex Paul |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Altshuler, Yaniv Pan, Wei Pentland, Alex Paul |
| author_sort | Altshuler, Yaniv |
| collection | MIT |
| description | The importance of the ability to predict trends in social media has been growing rapidly in the past few years with the growing dominance of social media in our everyday’s life. Whereas many works focus on the detection of anomalies in networks, there exist little theoretical work on the prediction of the likelihood of anomalous network pattern to globally spread and become “trends”. In this work we present an analytic model for the social diffusion dynamics of spreading network patterns. Our proposed method is based on information diffusion models, and is capable of predicting future trends based on the analysis of past social interactions between the community’s members. We present an analytic lower bound for the probability that emerging trends would successfully spread through the network. We demonstrate our model using two comprehensive social datasets — the Friends and Family experiment that was held in MIT for over a year, where the complete activity of 140 users was analyzed, and a financial dataset containing the complete activities of over 1.5 million members of the eToro social trading community. |
| first_indexed | 2024-09-23T16:44:28Z |
| format | Article |
| id | mit-1721.1/79923 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:44:28Z |
| publishDate | 2013 |
| publisher | Springer-Verlag |
| record_format | dspace |
| spelling | mit-1721.1/799232022-10-03T07:57:56Z Trends Prediction Using Social Diffusion Models Altshuler, Yaniv Pan, Wei Pentland, Alex Paul Massachusetts Institute of Technology. Media Laboratory Program in Media Arts and Sciences (Massachusetts Institute of Technology) Altshuler, Yaniv Pan, Wei Pentland, Alex Paul The importance of the ability to predict trends in social media has been growing rapidly in the past few years with the growing dominance of social media in our everyday’s life. Whereas many works focus on the detection of anomalies in networks, there exist little theoretical work on the prediction of the likelihood of anomalous network pattern to globally spread and become “trends”. In this work we present an analytic model for the social diffusion dynamics of spreading network patterns. Our proposed method is based on information diffusion models, and is capable of predicting future trends based on the analysis of past social interactions between the community’s members. We present an analytic lower bound for the probability that emerging trends would successfully spread through the network. We demonstrate our model using two comprehensive social datasets — the Friends and Family experiment that was held in MIT for over a year, where the complete activity of 140 users was analyzed, and a financial dataset containing the complete activities of over 1.5 million members of the eToro social trading community. 2013-08-22T18:06:34Z 2013-08-22T18:06:34Z 2012 Article http://purl.org/eprint/type/ConferencePaper 978-3-642-29046-6 978-3-642-29047-3 0302-9743 1611-3349 http://hdl.handle.net/1721.1/79923 Altshuler, Yaniv, Wei Pan, and Alex Pentland. Trends Prediction Using Social Diffusion Models. In Social Computing, Behavioral - Cultural Modeling and Prediction. S.J. Yang, A.M. Greenberg, and M. Endsley (Eds.) Springer-Verlag, pp. 97–104, 2012. (Lecture notes in computer science ; 7227) https://orcid.org/0000-0002-8053-9983 https://orcid.org/0000-0002-3410-9587 en_US http://dx.doi.org/10.1007/978-3-642-29047-3_12 Social Computing, Behavioral - Cultural Modeling and Prediction Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Springer-Verlag MIT Web Domain |
| spellingShingle | Altshuler, Yaniv Pan, Wei Pentland, Alex Paul Trends Prediction Using Social Diffusion Models |
| title | Trends Prediction Using Social Diffusion Models |
| title_full | Trends Prediction Using Social Diffusion Models |
| title_fullStr | Trends Prediction Using Social Diffusion Models |
| title_full_unstemmed | Trends Prediction Using Social Diffusion Models |
| title_short | Trends Prediction Using Social Diffusion Models |
| title_sort | trends prediction using social diffusion models |
| url | http://hdl.handle.net/1721.1/79923 https://orcid.org/0000-0002-8053-9983 https://orcid.org/0000-0002-3410-9587 |
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