Zhang, Q;
Cook, J;
Yilmaz, E;
(2021)
Detecting and Forecasting Misinformation via Temporal and Geometric Propagation Patterns.
In:
Advances in Information Retrieval. ECIR 2021.
(pp. pp. 455-462).
Springer: Cham, Switzerland.
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Abstract
Misinformation takes the form of a false claim under the guise of fact. It is necessary to protect social media against misinformation by means of effective misinformation detection and analysis. To this end, we formulate misinformation propagation as a dynamic graph, then extract the temporal evolution patterns and geometric features of the propagation graph based on Temporal Point Processes (TPPs). TPPs provide the appropriate modelling framework for a list of stochastic, discrete events. In this context, that is a sequence of social user engagements. Furthermore, we forecast the cumulative number of engaged users based on a power law. Such forecasting capabilities can be useful in assessing the threat level of misinformation pieces. By jointly considering the geometric and temporal propagation patterns, our model has achieved comparable performance with state-of-the-art baselines on two well known datasets.
| Type: | Proceedings paper |
|---|---|
| Title: | Detecting and Forecasting Misinformation via Temporal and Geometric Propagation Patterns |
| Event: | 43rd European Conference on IR Research, ECIR 2021 |
| ISBN-13: | 978-3-030-72239-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-72240-1_48 |
| Publisher version: | https://doi.org/10.1007/978-3-030-72240-1_48 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Misinformation, Propagation graph, Point processes |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10130145 |
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