The DOI matches its DataCite record; 1 of the 1 references it cites were found in a registry or an index.
Exploiting geolocation, user and temporal information for natural hazards monitoring in Twitter
Open accessAbstract
During emergency situation events it is important to acquire as much information about the event as possible, and social media sites like Twitter offer important real-time user contributed data. Typical Information Filtering techniques are keyword-based approaches or focused on co-occurrence with keywords. However, this approximations can lose relevant local information if messages do not contain an initially considered event-related keyword. Considering geolocation, user and temporal information within a pseudo-relevance feedback approach we can find event-related terminology but not co-occurring with initially considered keywords. Thus, taking into account the temporal aspect we can modify a query expansion function like Kullback-Leibler divergence in order to improve the Information Filtering process. The proposals has been evaluated in two datasets of real-world events obtaning encouraging results.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Research in expanding data collection is still in its infancy, and query expansion approaches such as pseudo-relevance feedback are yet to be explored in detail, beyond preliminary research showing potential.
“Research in expanding data collection is still in its infancy and the use of query expansion approaches through techniques, such as pseudo-relevance feedback, are yet to be explored in detail, further to preliminary research showing its potential (Fresno et al. 2015).”
From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Fresno et al. 2015
The Introduction and Conclusions describe this Twitter natural-hazard query-expansion approach as preliminary, report promising results, and call for further study. But Section 3 says pseudo-relevance feedback has been effective in many information-retrieval tasks; the paper does not establish the broader claim that query-expansion research is generally in its infancy or unexplored in detail. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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
Processing Social Media Messages in Mass Emergency: A Survey
Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015) · ACM Computing Surveys · doi:10.1145/2771588 · Cited by 804
Flagged first, then by citations
Research works printed but not linked: 20
- Carpineto, C., R. de Mori, G. Romano, and B. Bigi. 2001. An information-theoretic approach to automatic query expansion. ACM Trans. Inf. Syst., 19(1):1-27.
- De Longueville, B., R.S. Smith, and G. Lu- raschi. 2009. OMG, from here, i can see the flames!: A use case of mining location based social networks to acquire spatio- temporal data on forest fires. In Procee- dings of the 2009 International Workshop on Location Based Social Networks, LBSN '09, pages 73-80, New York, NY, USA. ACM.
- Deerwester, S., S.T. Dumais, G.W. Fur- nas, T.K. Landauer, and R. Harshman. 1990. Indexing by latent semantic analy- sis. Journal of the American society for information science, 41(6):391-407.
- Hiemstra, D. 2009. Information Retrieval Models. Information Retrieval: Searching in the 21st Century, Wiley.
- Hughes, A.L. and L. Palen. 2009. Twitter adoption and use in mass convergence and emergency events. International Journal of Emergency Management, 6(3):248-260.
- Kullback, S. and R.A. Leibler. 1951. On information and sufficiency. Ann. Math. Statist., 22(1):79-86.
- Mills, A., R. Chen, J. Lee, and H. R. Rao. 2009. Web 2.0 emergency applications: How useful can twitter be for emergency response. Journal of Information Privacy and Security, 5:3-26.
- Palen, L., K. Anderson, G. Mark, J. Mar- tin, D. Sicker, and D. Grunwald. 2010. A vision for technology-mediated public participation and assistance in mass emer- gencies and disasters. In The University of Edinburgh, editor, International Aca- demic Research Conference, 14-16 April.
- Sakaki, T., M. Okazaki, and Y. Matsuo. 2010. Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on World wide web, pages 851- 860. ACM.
- Sasaki, K., S. Nagano, K. Ueno, and K. Cho. 2012. Feasibility study on detection of transportation information exploiting twitter as a sensor. In Sixth International AAAI Conference on Weblogs and Social Media. Workshop on When the City Meets the Citizen, AAAI Technical Report WS- 12-04.
- Schnebele, E. and G. Cervone. 2013. Im- proving remote sensing flood assessment using volunteered geographical data. Na- tural Hazards and Earth System Science, 13(3):669-677.
- Shinohara, M., A. Hattori, S. Ioroi, H. Ta- naka, H. Hayami, H. Fujioka, and Y. Ha- rada. 2011. Design and trial of a cell- phone-based hazard information sharing system for residents living close to an in- cident. In Next Generation Mobile Appli- cations, Services and Technologies (NG- MAST), 2011 5th International Conferen- ce on, pages 13-18, Sept.
- Shklovski, I., L. Palen, and J. Sutton. 2008. Finding community through information and communication technology in disas- ter events. In Proceedings of the ACM 2008 Conference on Computer Suppor- ted Cooperative Work (CSCW 2008, pages 127-136.
- Sinnappan, S., C. Farrell, and E. Stewart. 2010. Priceless tweets! a study on twit- ter messages posted during crisis: Black saturday. In Proceedings of Information Systems: Defining and Establishing a High Impact Discipline, number 39, Brisba- ne, Australia, 01-03 December 2010. 21st International Conference on Information Systems (ACIS 2010).
- Sorensen, J.H. and B. Sorensen. 2007. Com- munity processes: Warning and evacua- tion. In Handbook of Disaster Research, Handbooks of Sociology and Social Re- search. Springer New York, pages 183- 199.
- Starbird, K., L. Palen, A.L. Hughes, and S. Vieweg. 2010. Chatter on the red: What hazards threat reveals about the social life of microblogged information. In Proceedings of the 2010 ACM Confe- rence on Computer Supported Cooperati- ve Work, CSCW '10, pages 241-250, New York, NY, USA. ACM.
- Steyvers, M. and T. Griffiths, 2007. Latent Semantic Analysis: A Road to Meaning, chapter Probabilistic topic models. Erl- baum, L.
- Sutton, J., L. Palen, and I. Shklovki. 2008. Backchannels on the front lines: Emergent use of social media in the 2007 southern california fires. In Proceedings of the 2008 Information Systems for Crisis Response and Management Conference (ISCRAM 2008), pages 624-631, Washington, D.C. Vieweg, S., A.L. Hughes, K. Starbird, and L. Palen. 2010. Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 1079-1088. ACM.
- Xu, J. and W.B. Croft. 1996. Query ex- pansion using local and global document analysis. In Proceedings of SIGIR 96, pa- ges 4-11, Zurich. ACM.
- Zubiaga, A., D. Spina, V. Fresno, and R. Martínez. 2011. Classifying trending topics: a typology of conversation triggers on twitter. In C. Macdonald, I. Ounis, and I. Ruthven, editors, CIKM, pages 2461- 2464. ACM.
Cited by
Sorted by citations
Detection and Resolution of Rumours in Social Media: A Survey
Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2019) · ACM Computing Surveys · doi:10.1145/3161603 · Cited by 862
Self-citation
Metadata
- Authors
- Fresno, V., Zubiaga, A., Ji, H., & Martínez, R.
- Venue
- University of Warwick
- Publisher
- University of Warwick
- Published
- 2015-01-01
- Volume
- 54
- Issue
- 54
- Pages
- 85-92
- OpenAlex
- W1579601000
- Cited by
- 6
- References
- 1
- Citations by year
- 2019: 3 · 2018: 1 · 2017: 1 · 2016: 1
- FWCI
- 3.09
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- RUA, Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante) (submittedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion)
- Redalyc (Universidad Autónoma del Estado de México) (submittedVersion)
- University of Warwick (cc-by)
- Procesamiento del lenguaje natural
- Funders
- Ministerio de Ciencia e Innovación (TIN2013-46616-C2-2-R)
- Universidad Nacional de Educación a Distancia
- Army Research Laboratory (W911NF, W911NF-09-2-0053)
- Topics
- Social Sciences › Communication › Public Relations and Crisis Communication
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Keywords
- Twitter, emergency situations, social media, information filtering, pseudo-relevance feedback, geolocation, temporal information, user data, query expansion, Kullback-Leibler divergence
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
