期刊
MATHEMATICS
卷 9, 期 22, 页码 -出版社
MDPI
DOI: 10.3390/math9222988
关键词
fake news detection; social networks; false information; machine learning; data mining
类别
资金
- Fundacao para a Ciencia e Tecnologia (FCT), Portugal [SFRH/BD/129708/2017]
- Canada Research Chairs program
- NSERC
- Fundação para a Ciência e a Tecnologia [SFRH/BD/129708/2017] Funding Source: FCT
Current research on false information in social networks mainly focuses on short-term detection in specific contexts, lacking long-term evaluation of existing proposals. Training detection models with word-embedding features proves to be more effective and less affected by topic changes.
The negative impact of false information on social networks is rapidly growing. Current research on the topic focused on the detection of fake news in a particular context or event (such as elections) or using data from a short period of time. Therefore, an evaluation of the current proposals in a long-term scenario where the topics discussed may change is lacking. In this work, we deviate from current approaches to the problem and instead focus on a longitudinal evaluation using social network publications spanning an 18-month period. We evaluate different combinations of features and supervised models in a long-term scenario where the training and testing data are ordered chronologically, and thus the robustness and stability of the models can be evaluated through time. We experimented with 3 different scenarios where the models are trained with 15-, 30-, and 60-day data periods. The results show that detection models trained with word-embedding features are the ones that perform better and are less likely to be affected by the change of topics (for example, the rise of COVID-19 conspiracy theories). Furthermore, the additional days of training data also increase the performance of the best feature/model combinations, although not very significantly (around 2%). The results presented in this paper build the foundations towards a more pragmatic approach to the evaluation of fake news detection models in social networks.
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