%0 Journal Article %1 Weng2013Virality %A Weng, Lilian %A Menczer, Filippo %A Ahn, Yong-Yeol %D 2013 %J Scientific Reports %K social-networks information-diffusion complex-contagion %R 10.1038/srep02522 %T Virality Prediction and Community Structure in Social Networks %U http://dx.doi.org/10.1038/srep02522 %V 3 %X How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed behave like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection may lead to significant advances in computational social science, social media analytics, and marketing applications.

@article{Weng2013Virality, abstract = {{How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed behave like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection may lead to significant advances in computational social science, social media analytics, and marketing applications.}}, added-at = {2019-06-10T14:53:09.000+0200}, archiveprefix = {arXiv}, author = {Weng, Lilian and Menczer, Filippo and Ahn, Yong-Yeol}, biburl = {https://www.bibsonomy.org/bibtex/22ef0c468f4733f3fc8624e80077ae9c6/nonancourt}, citeulike-article-id = {12600026}, citeulike-linkout-0 = {http://arxiv.org/abs/1306.0158}, citeulike-linkout-1 = {http://arxiv.org/pdf/1306.0158}, citeulike-linkout-2 = {http://dx.doi.org/10.1038/srep02522}, day = 28, doi = {10.1038/srep02522}, eprint = {1306.0158}, interhash = {c0d125e1ee3c6cc35aaeed7895518cfa}, intrahash = {2ef0c468f4733f3fc8624e80077ae9c6}, issn = {2045-2322}, journal = {Scientific Reports}, keywords = {social-networks information-diffusion complex-contagion}, month = aug, posted-at = {2013-08-28 20:03:47}, priority = {2}, timestamp = {2019-08-22T16:25:27.000+0200}, title = {{Virality Prediction and Community Structure in Social Networks}}, url = {http://dx.doi.org/10.1038/srep02522}, volume = 3, year = 2013 }