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Eventfully Safapp: hybrid approach to event detection for social media mining

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Abstract

Safapp is a tool that crawls and analyses data from social media (specially Twitter and blogs) in the context of radicalization detection. This app has been developed in the context of SAFFRON European project. This paper focuses on the description of the semantic module of Safapp which is dedicated to the analysis of textual content of social networks and blogs, and more specifically on a newly developed module: the event extractor. With this module we expect to go one step further in the development of useful tools to track and analyze online propaganda.

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Notes

  1. http://www.saffron-project.eu/en/home/.

  2. MUC: Message understanding conferences (MUC) initiated and financed by DARPA (Defense Advanced Research Projects Agency) to encourage the development of new and better methods of information extraction. The MUC-4 topic was Terrorist activities in Latin America (Source: Wikipedia).

  3. REES: https://dl.acm.org/citation.cfm?id=974158.

  4. Synonyms extracted from Wordnet.

  5. We first focus on events extracted from blogs because they tend to be more complete in terms of observed arguments. Although some events can be found in Twitter, they present normally very few arguments.

  6. http://brat.nlplab.org/.

  7. https://github.com/savkov/BratUtils.

  8. https://nlp.stanford.edu/software/classifier.html.

  9. http://www.chokkan.org/software/crfsuite/.

  10. http://www.cs.cmu.edu/ark/TweetNLP/.

  11. https://nlp.stanford.edu/software/tregex.shtml.

  12. https://nlp.stanford.edu/software/tokensregex.html.

References

  • Agraval R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on management of data’93. ACM, New York, pp 207–216

  • Aone C, Ramos-Santacruz M (2000) Rees: a large-scale relation and event extraction system. In: ANLP, pp 76–83

  • Baker CF, Fillmore CJ, Lowe JB (1998) The Berkeley FrameNet project. In: Proceedings of the COLING/ACL, Montreal, pp 86–90

  • Becker H, Naaman M, Gravano L (2011) Beyond trending topics: real-world event identification on Twitter. Columbia University Academic Commons

  • Boros E, Besançon R, Ferret O, Grau G (2014) Etiquetage en rôles évènementiels fondé sur l’utilisation d’un modèle neuronal. In: TALN 2014, Marseille, pp 25–35

  • Carter JA, Maher S, Neumann PR (2014) Measuring importance and influence in syrian foreign fighter networks. In: The international centre for the study of radicalisation and political violence

  • Chambers N, Cer D, Grenager T, Hall D, Kiddon C, MacCartney B, de Marneffe M-C, Ramage D, Yeh E, Manning CD (2007) Learning alignments and leveraging natural logic. In: Proceedings of the workshop on textual entailment and paraphrasing, pp 165–170

  • Chieu HL, Ng HT, Lee YK (2003) Closing the gap: learning-based information extraction rivaling knowledge-engineering methods. In: 41st annual meeting of the association for computational linguistics (ACL’03), pp 216–223

  • Derbas N, Segond F, Padró M, Dusserre E, Dobre T, Monaci S, Mastrobuoni G (2017) Semantic analysis supporting de-radicalisation. In: Lyon: 28th international workshop on database and expert systems applications (DEXA)

  • Grishman R, Westbrook D, Meyers A (2005) NYU’s English ACE 2005 system description. ACE, 2005

  • Okazaki N (2007) CRFsuite: a fast implementation of conditional random fields (CRFs). http://www.chokkan.org/software/crfsuite

  • Owoputi O, Dyer C, Gimpel K, Schneider N (2012) Part-of-speech tagging for Twitter: word clusters and other advances. Technical report, CMU

  • Riloff E (1996a) Automatically generating extraction patterns from untagged text. In: AAAI’96, pp 1044–1049

  • Sakaki T, Ozaki M, Matsuo Y (2010) Earthquake shakes Twitter users: real-time event detection by social sensors. In: Conference on world wide web, WWW’10. ACM, New York, pp 851–860

  • Sankaranarayanan J, Samet H, Teitler B, Teitler BE, Sperling J (2009) TwitterStand: news in tweets. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, GIS’09. ACM, New York, pp 42–51

  • Serrano L, Charnois T, Brunessaux S, Grilhères B, Bouzid M (2012) Combinaison d’approches pour l’extraction automatique d’évènements. In: 19eme conférence sur le Traitement Automatique des Langues Naturelles (TALN 2012), Grenoble, pp 423–430

  • Vapnik VN (1998) Statistical learning theory. Wiley Interscience, New York

    MATH  Google Scholar 

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Authors and Affiliations

  1. Viseo Research, Grenoble, France

    Nadia Derbas & Frédérique Segond

  2. Eloquant, Grenoble, France

    Emmanuelle Dusserre & Muntsa Padró

  3. INALCO ERTIM, Paris, France

    Frédérique Segond

Authors
  1. Nadia Derbas
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  2. Emmanuelle Dusserre
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  3. Muntsa Padró
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  4. Frédérique Segond
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Correspondence to Muntsa Padró.

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Derbas, N., Dusserre, E., Padró, M. et al. Eventfully Safapp: hybrid approach to event detection for social media mining. J Ambient Intell Human Comput 11, 87–95 (2020). https://doi.org/10.1007/s12652-018-1078-7

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