The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities
Abstract
Current social media research mainly focuses on temporal trends of the information flow and on the topology of the social graph that facilitates the propagation of information. In this paper we study the effect of the content of the idea on the information propagation. We present an efficient hybrid approach based on a linear regression for predicting the spread of an idea in a given time frame. We show that a combination of content features with temporal and topological features minimizes prediction error.
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A Simplex Method for Function Minimization
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The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652
The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods
Tausczik, Y. R., & Pennebaker, J. W. (2010) · Journal of Language and Social Psychology · doi:10.1177/0261927x09351676 · Cited by 5,774
Measuring user influence in twitter: The million follower fallacy
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14033 · Cited by 3,022
Cited by
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Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14401 · Cited by 863
A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields
Ko, H., Lee, S., Park, Y., & Choi, A. (2022) · Electronics · doi:10.3390/electronics11010141 · Cited by 658
Bots increase exposure to negative and inflammatory content in online social systems
Stella, M., Ferrara, E., & De Domenico, M. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1803470115 · Cited by 492
CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises
Olteanu, A., Castillo, C., Diaz, F., & Vieweg, S. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14538 · Cited by 397
Accelerated Attributed Network Embedding
Huang, X., Li, J., & Hu, X. (2017) · Proceedings of the 2017 SIAM International Conference on Data Mining · doi:10.1137/1.9781611974973.71 · Cited by 311
Metadata
- Authors
- Tsur, O., & Rappoport, A.
- Venue
- Proceedings of the fifth ACM international conference on Web search and data mining
- Publisher
- ACM
- Published
- 2012-02-08
- Pages
- 643-652
- OpenAlex
- W2114544578
- Cited by
- 423
- References
- 6
- FWCI
- 31.00
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Tsur, O., & Rappoport, A. (2012). What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities. Proceedings of the fifth ACM international conference on Web search and data mining. https://doi.org/10.1145/2124295.2124320
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