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Tweet2Story: A Web App to Extract Narratives from Twitter

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Advances in Information Retrieval (ECIR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13186))

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Abstract

Social media platforms are used to discuss current events with very complex narratives that become difficult to understand. In this work, we introduce Tweet2Story, a web app to automatically extract narratives from small texts such as tweets and describe them through annotations. By doing this, we aim to mitigate the difficulties existing on creating narratives and give a step towards deeply understanding the actors and their corresponding relations found in a text. We build the web app to be modular and easy-to-use, which allows it to easily incorporate new techniques as they keep getting developed.

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Notes

  1. 1.

    in the sentence “Steve Jobs was the CEO of Apple”, the entity “Steve Jobs” fits the category of “person”.

  2. 2.

    the expression “last year” would be parsed as “2020” as of this writing.

  3. 3.

    in the sentence “Sally lives in Paris. She lives in France”, both “Sally” and “She” refer to the same entity and, therefore, belong to the same cluster.

  4. 4.

    in the sentence “Sally lives in Paris”, the event is expressed through the verb “lives”.

  5. 5.

    in the sentence “Sally lives in Paris” the triple “Sally - lives - in Paris” is categorized as a location triple.

  6. 6.

    https://github.com/LIAAD/Tweet2Story-demo.

  7. 7.

    http://tweet2story.inesctec.pt/.

  8. 8.

    https://brat.nlplab.org/standoff.html.

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Acknowledgements

Vasco Campos and Pedro Mota were financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020 and LA/P/0063/2020. Ricardo Campos and Alípio Jorge were financed by the ERDF - European Regional Development Fund through the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project PTDC/CCI-COM/31857/2017 (NORTE-01-0145-FEDER-03185). This funding fits under the research line of the Text2Story project.

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Authors and Affiliations

  1. INESC TEC, Porto, Portugal

    Vasco Campos, Ricardo Campos, Pedro Mota & Alípio Jorge

  2. FCUP, University of Porto, Porto, Portugal

    Vasco Campos, Pedro Mota & Alípio Jorge

  3. Polytechnic Institute of Tomar, Ci2 - Smart Cities Research Center, Tomar, Portugal

    Ricardo Campos

Authors
  1. Vasco Campos
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  2. Ricardo Campos
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  4. Alípio Jorge
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Correspondence to Vasco Campos .

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Editors and Affiliations

  1. Martin Luther University Halle-Wittenberg, Halle, Germany

    Matthias Hagen

  2. Leiden University, Leiden, The Netherlands

    Suzan Verberne

  3. University of Glasgow, Glasgow, UK

    Craig Macdonald

  4. University of Duisburg-Essen, Essen, Germany

    Christin Seifert

  5. University of Stavanger, Stavanger, Norway

    Krisztian Balog

  6. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  7. University of Stavanger, Stavanger, Norway

    Vinay Setty

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Campos, V., Campos, R., Mota, P., Jorge, A. (2022). Tweet2Story: A Web App to Extract Narratives from Twitter. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_32

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  • DOI: https://doi.org/10.1007/978-3-030-99739-7_32

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