The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Dynamical classes of collective attention in twitter
Open accessAbstract
Micro-blogging systems such as Twitter expose digital traces of social discourse with an unprecedented degree of resolution of individual behaviors. They offer an opportunity to investigate how a large-scale social system responds to exogenous or endogenous stimuli, and to disentangle the temporal, spatial and topical aspects of users' activity. Here we focus on spikes of collective attention in Twitter, and specifically on peaks in the popularity of hashtags. Users employ hashtags as a form of social annotation, to define a shared context for a specific event, topic, or meme. We analyze a large-scale record of Twitter activity and find that the evolution of hashtag popularity over time defines discrete classes of hashtags. We link these dynamical classes to the events the hashtags represent and use text mining techniques to provide a semantic characterization of the hashtag classes. Moreover, we track the propagation of hashtags in the Twitter social network and find that epidemic spreading plays a minor role in hashtag popularity, which is mostly driven by exogenous factors.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
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 1×
“Several aspects of Twitter have been extensively investigated in the literature, including its network topology [18, 19, 20], the relations and types of messages between users [21, 22], the internal information propagation [23], the credibility of information [24, 25], and even its potential as an indicator of the sta…”
BackgroundInformation contagion: an empirical study of the spread of news on digg and twitter social networks
Lerman, K., & Ghosh, R. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14021 · Cited by 813
Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter
Dodds, P. S., Harris, K. D., Kloumann, I. M., Bliss, C. A., & Danforth, C. M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0026752 · Cited by 809
Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number
Gonçalves, B., Perra, N., & Vespignani, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022656 · Cited by 638
Self-citationPredicting consumer behavior with web search
Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M., & Watts, D. J. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1005962107 · Cited by 636
The dynamics of protest recruitment through an online network
González-Bailón, S., Borge-Holthoefer, J., Rivero, A., & Moreno, Y. (2011) · Scientific Reports · doi:10.1038/srep00197 · Cited by 571
Ratkiewicz (2011) · doi:10.1145/1963192.1963301 · Cited by 541
Traffic in Social Media II: Modeling Bursty Popularity
Ratkiewicz, J., Menczer, F., Fortunato, S., Flammini, A., & Vespignani, A. (2010) · 2010 IEEE Second International Conference on Social Computing · doi:10.1109/socialcom.2010.63 · Cited by 38
Cited by
Sensing Trending Topics in Twitter
Aiello, L. M., Petkos, G., Martin, C., Corney, D., Papadopoulos, S., Skraba, R., Goker, A., Kompatsiaris, I., & Jaimes, A. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2265080 · Cited by 431
The Effects of Twitter Sentiment on Stock Price Returns
Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., & Mozetič, I. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138441 · Cited by 395
Spontaneous Collective Action: Peripheral Mobilization During the Arab Spring
STEINERT-THRELKELD, Z. C. (2017) · American Political Science Review · doi:10.1017/s0003055416000769 · Cited by 331
Understanding the Factors That Impact the Popularity of GitHub Repositories
Borges, H., Hora, A., & Valente, M. T. (2016) · 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME) · doi:10.1109/icsme.2016.31 · Cited by 312
The spontaneous emergence of conventions: An experimental study of cultural evolution
Centola, D., & Baronchelli, A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418838112 · Cited by 282
Metadata
- Authors
- Lehmann, J., Gonçalves, B., Ramasco, J. J., & Cattuto, C.
- Venue
- Proceedings of the 21st international conference on World Wide Web
- Publisher
- ACM
- Published
- 2012-04-16
- Pages
- 251-260
- OpenAlex
- W2020221730
- Cited by
- 380
- References
- 9
- FWCI
- 44.96
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Dynamical classes of collective attention in twitter. https://app.cochat.ai/papers/10.1145/2187836.2187871
- The paper
- Lehmann, J., Gonçalves, B., Ramasco, J. J., & Cattuto, C. (2012). Dynamical classes of collective attention in twitter. Proceedings of the 21st international conference on World Wide Web. https://doi.org/10.1145/2187836.2187871
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
