The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Emerging topic detection on Twitter based on temporal and social terms evaluation
Open accessAbstract
Twitter is a user-generated content system that allows its users to share short text messages, called tweets, for a vari-ety of purposes, including daily conversations, URLs shar-ing and information news. Considering its world-wide dis-tributed network of users of any age and social condition, it represents a low level news flashes portal that, in its impres-sive short response time, has the principal advantage. In this paper we recognize this primary role of Twitter and we propose a novel topic detection technique that permits to retrieve in real-time the most emergent topics expressed by the community. First, we extract the contents (set of terms) of the tweets and model the term life cycle according to a novel aging theory intended to mine the emerging ones. A term can be defined as emerging if it frequently occurs in the specified time interval and it was relatively rare in the past. Moreover, considering that the importance of a content also depends on its source, we analyze the social relationships in the network with the well-known Page Rank algorithm in order to determine the authority of the users. Finally, we leverage a navigable topic graph which connects the emerg-ing terms with other semantically related keywords, allowing the detection of the emerging topics, under user-specified time constraints. We provide different case studies which show the validity of the proposed approach.
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
(1999) · Cited by 12,854
Term-weighting approaches in automatic text retrieval
Salton, G., & Buckley, C. (1988) · Information Processing & Management · doi:10.1016/0306-4573(88)90021-0 · Cited by 9,740
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Using collaborative filtering to weave an information tapestry
Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992) · Communications of the ACM · doi:10.1145/138859.138867 · Cited by 4,066
Earthquake shakes Twitter users: real-time event detection by social sensors
Sakaki, T., Okazaki, M., & Matsuo, Y. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772777 · Cited by 3,640
Fab: content-based, collaborative recommendation
Balabanović, M., & Shoham, Y. (1997) · Communications of the ACM · doi:10.1145/245108.245124 · Cited by 2,934
Leskovec, J., & Faloutsos, C. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150479 · Cited by 1,170
Content-boosted collaborative filtering for improved recommendations
Melville, P., Mooney, R. J., & Nagarajan, R. (2002) · doi:10.5555/777092.777124 · Cited by 1,084
Topic detection and tracking: event-based information organization
Allan, J. (2002) · Cited by 682
Tag Recommendations in Folksonomies
Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., & Stumme, G. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-74976-9_52 · Cited by 508
Cited by
Social big data: recent achievements and new challenges
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016) · Information Fusion · doi:10.1016/j.inffus.2015.08.005 · Cited by 846
“, LDA, Gibbs sampling, and the TSTE method [85]).”
MethodInformation diffusion in online social networks: a survey
Guille, A., Hacid, H., Favre, C., & Zighed, D. A. (2013) · ACM SIGMOD Record · doi:10.1145/2503792.2503797 · Cited by 851
Tweet Analysis for Real-Time Event Detection and Earthquake Reporting System Development
Sakaki, T., Okazaki, M., & Matsuo, Y. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.29 · Cited by 502
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
Twitter for crisis communication: lessons learned from Japan's tsunami disaster
Acar, A., & Muraki, Y. (2011) · International Journal of Web Based Communities · doi:10.1504/ijwbc.2011.041206 · Cited by 419
Metadata
- Authors
- Cataldi, M., Di Caro, L., & Schifanella, C.
- Venue
- Proceedings of the Tenth International Workshop on Multimedia Data Mining
- Publisher
- ACM
- Published
- 2010-07-25
- Pages
- 1-10
- OpenAlex
- W1983012012
- Cited by
- 537
- References
- 22
- FWCI
- 27.55
- 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: Emerging topic detection on Twitter based on temporal and social terms evaluation. https://app.cochat.ai/papers/10.1145/1814245.1814249
- The paper
- Cataldi, M., Di Caro, L., & Schifanella, C. (2010). Emerging topic detection on Twitter based on temporal and social terms evaluation. Proceedings of the Tenth International Workshop on Multimedia Data Mining. https://doi.org/10.1145/1814245.1814249
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.
