1,519 research outputs found
FakeSwarm: Improving Fake News Detection with Swarming Characteristics
The proliferation of fake news poses a serious threat to society, as it can
misinform and manipulate the public, erode trust in institutions, and undermine
democratic processes. To address this issue, we present FakeSwarm, a fake news
identification system that leverages the swarming characteristics of fake news.
To extract the swarm behavior, we propose a novel concept of fake news swarming
characteristics and design three types of swarm features, including principal
component analysis, metric representation, and position encoding. We evaluate
our system on a public dataset and demonstrate the effectiveness of
incorporating swarm features in fake news identification, achieving an f1-score
and accuracy of over 97% by combining all three types of swarm features.
Furthermore, we design an online learning pipeline based on the hypothesis of
the temporal distribution pattern of fake news emergence, validated on a topic
with early emerging fake news and a shortage of text samples, showing that
swarm features can significantly improve recall rates in such cases. Our work
provides a new perspective and approach to fake news detection and highlights
the importance of considering swarming characteristics in detecting fake news.Comment: 9th International Conference on Data Mining and Applications (DMA
2023). Keywords: Fake News Detection, Metric Learning, Clustering,
Dimensionality Reductio
MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter
To alleviate the impact of fake news on our society, predicting the popularity of fake news posts on social media is a crucial problem worthy of study. However, most related studies on fake news emphasize detection only. In this paper, we focus on the issue of fake news influence prediction, i.e., inferring how popular a fake news post might become on social platforms. To achieve our goal, we propose a comprehensive framework, MUFFLE, which captures multi-modal dynamics by encoding the representation of news-related social networks, user characteristics, and content in text. The attention mechanism developed in the model can provide explainability for social or psychological analysis. To examine the effectiveness of MUFFLE, we conducted extensive experiments on real-world datasets. The experimental results show that our proposed method outperforms both state-of-the-art methods of popularity prediction and machine-based baselines in top-k NDCG and hit rate. Through the experiments, we also analyze the feature importance for predicting fake news influence via the explainability provided by MUFFLE
DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection
Fake News on social media platforms has attracted a lot of attention in
recent times, primarily for events related to politics (2016 US Presidential
elections), healthcare (infodemic during COVID-19), to name a few. Various
methods have been proposed for detecting Fake News. The approaches span from
exploiting techniques related to network analysis, Natural Language Processing
(NLP), and the usage of Graph Neural Networks (GNNs). In this work, we propose
DEAP-FAKED, a knowleDgE grAPh FAKe nEws Detection framework for identifying
Fake News. Our approach is a combination of the NLP -- where we encode the news
content, and the GNN technique -- where we encode the Knowledge Graph (KG). A
variety of these encodings provides a complementary advantage to our detector.
We evaluate our framework using two publicly available datasets containing
articles from domains such as politics, business, technology, and healthcare.
As part of dataset pre-processing, we also remove the bias, such as the source
of the articles, which could impact the performance of the models. DEAP-FAKED
obtains an F1-score of 88% and 78% for the two datasets, which is an
improvement of 21%, and 3% respectively, which shows the effectiveness of the
approach.Comment: Accepted at IEEE/ACM International Conference on Advances in Social
Networks Analysis and Mining (ASONAM) 202
Fake News Detection with Heterogeneous Transformer
The dissemination of fake news on social networks has drawn public need for
effective and efficient fake news detection methods. Generally, fake news on
social networks is multi-modal and has various connections with other entities
such as users and posts. The heterogeneity in both news content and the
relationship with other entities in social networks brings challenges to
designing a model that comprehensively captures the local multi-modal semantics
of entities in social networks and the global structural representation of the
propagation patterns, so as to classify fake news effectively and accurately.
In this paper, we propose a novel Transformer-based model: HetTransformer to
solve the fake news detection problem on social networks, which utilises the
encoder-decoder structure of Transformer to capture the structural information
of news propagation patterns. We first capture the local heterogeneous
semantics of news, post, and user entities in social networks. Then, we apply
Transformer to capture the global structural representation of the propagation
patterns in social networks for fake news detection. Experiments on three
real-world datasets demonstrate that our model is able to outperform the
state-of-the-art baselines in fake news detection
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