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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2022: Machine Learning and Knowledge Discovery in Databases pp 671–687Cite as

MultiLayerET: A Unified Representation of Entities and Topics Using Multilayer Graphs

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13714))

Abstract

Many online news outlets, forums, and blogs provide a rich stream of publications and user comments. This rich body of data is a valuable source of information for researchers, journalists, and policymakers. However, the ever-increasing production and user engagement rate make it difficult to analyze this data without automated tools. This work presents MultiLayerET, a method to unify the representation of entities and topics in articles and comments. In MultiLayerET, articles’ content and associated comments are parsed into a multilayer graph consisting of heterogeneous nodes representing named entities and news topics. The nodes within this graph have attributed edges denoting weight, i.e., the strength of the connection between the two nodes, time, i.e., the co-occurrence contemporaneity of two nodes, and sentiment, i.e., the opinion (in aggregate) of an entity toward a topic. Such information helps in analyzing articles and their comments. We infer the edges connecting two nodes using information mined from the textual data. The multilayer representation gives an advantage over a single-layer representation since it integrates articles and comments via shared topics and entities, providing richer signal points about emerging events. MultiLayerET can be applied to different downstream tasks, such as detecting media bias and misinformation. To explore the efficacy of the proposed method, we apply MultiLayerET to a body of data gathered from six representative online news outlets. We show that with MultiLayerET, the classification F1 score of a media bias prediction model improves by \(36\%\), and that of a state-of-the-art fake news detection model improves by \(4\%\).

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Notes

  1. 1.

    https://www.wikidata.org/.

  2. 2.

    Full article: https://wapo.st/3yOMYdO.

  3. 3.

    https://textblob.readthedocs.io/en/dev/.

  4. 4.

    https://www.allsides.com/media-bias/media-bias-ratings.

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Acknowledgements

This research was supported in part by the U.S. NSF awards 2026513 and 1838145, and the ARL subaward 555080-78055 under Prime Contract No. W911NF2220001 and Temple University office of the Vice President for Research 2022 Catalytic Collaborative Research Initiative Program. AI & ML Focus Area. In addition, this research includes calculations carried out on HPC resources supported in part by the U.S. NSF through major research instrumentation grant number 1625061 and by the U.S. Army Research Laboratory under contract number W911NF-16-2-0189.

Author information

Authors and Affiliations

  1. Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, PA, USA

    Jumanah Alshehri, Marija Stanojevic, Parisa Khan, Benjamin Rapp, Eduard Dragut & Zoran Obradovic

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  1. Jumanah Alshehri
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  2. Marija Stanojevic
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  3. Parisa Khan
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  4. Benjamin Rapp
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  5. Eduard Dragut
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  6. Zoran Obradovic
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Corresponding author

Correspondence to Jumanah Alshehri .

Editor information

Editors and Affiliations

  1. Grenoble Alpes University, Saint Martin d'Hères, France

    Massih-Reza Amini

  2. INSA Rouen Normandy, Saint Etienne du Rouvray, France

    Stéphane Canu

  3. Ruhr-Universität Bochum, Bochum, Germany

    Asja Fischer

  4. KU Leuven, Leuven, Belgium

    Tias Guns

  5. Central European University, Vienna, Austria

    Petra Kralj Novak

  6. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Grigorios Tsoumakas

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Alshehri, J., Stanojevic, M., Khan, P., Rapp, B., Dragut, E., Obradovic, Z. (2023). MultiLayerET: A Unified Representation of Entities and Topics Using Multilayer Graphs. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13714. Springer, Cham. https://doi.org/10.1007/978-3-031-26390-3_39

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  • DOI: https://doi.org/10.1007/978-3-031-26390-3_39

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  • Online ISBN: 978-3-031-26390-3

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