2,682 research outputs found
Neural language model based training data augmentation for weakly supervised early rumor detection
The scarcity and class imbalance of training data are known issues in current rumor detection tasks. We propose a straight-forward and general-purpose data augmentation technique which is beneficial to early rumor detection relying on event propagation patterns. The key idea is to exploit massive unlabeled event data sets on social media to augment limited labeled rumor source tweets. This work is based on rumor spreading patterns revealed by recent rumor studies and semantic relatedness between labeled and unlabeled data. A state-of-the-art neural language model (NLM) and large credibility-focused Twitter corpora are employed to learn context-sensitive representations of rumor tweets. Six different real-world events based on three publicly available rumor datasets are employed in our experiments to provide a comparative evaluation of the effectiveness of the method. The results show that our method can expand the size of an existing rumor data set nearly by 200% and corresponding social context (i.e., conversational threads) by 100% with reasonable quality. Preliminary experiments with a state-of-the-art deep learning-based rumor detection model show that augmented data can alleviate over-fitting and class imbalance caused by limited train data and can help to train complex neural networks (NNs). With augmented data, the performance of rumor detection can be improved by 12.1% in terms of F-score. Our experiments also indicate that augmented training data can help to generalize rumor detection models on unseen rumors
Event Based Rumor Detection on Social Media for Digital Forensics and Information Security
Advancement in information technology such as social networking is on one side is powerful source of news and information and on other side have posed new challenges for those policing cybercrime. Cybercriminals and terrorists are spreading rumors that is unreal or even malicious information on social network which can bring massive panic and social unrest to our community. The rumor detection problem on social network has attracted considerable attention in recent years. A different type of rumors has different characteristics and need different techniques and approaches to detect. In this paper, we proposed an efficient approach to detect event based rumor on social media like Twitter. Experiment illustrates that our event based rumor detection method obtain significant improvement compared with the previous work
Sentiment aware fake news detection on online social networks
Messages posted to online social networks (OSNs) causes a
recent stir due to the intended spread of fake news or rumor.
In this work, we aim to understand and analyse the characteristics
of fake news especially in relation to sentiments, to
determine the automatic detection of fake news and rumors.
Based on empirical observation, we propose a hypothesis
that there exists a relation between a fake message/rumour
and the sentiment of the texts posted online. We verify our
hypothesis by comparing with the state-of-the-art baseline
text-only fake news detection methods that do not consider
sentiments. We performed experiments on standard Twitter
fake news dataset and show good improvements in detecting
fake news/rumor
A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning
The diffusion of rumors on microblogs generally follows a propagation tree
structure, that provides valuable clues on how an original message is
transmitted and responded by users over time. Recent studies reveal that rumor
detection and stance detection are two different but relevant tasks which can
jointly enhance each other, e.g., rumors can be debunked by cross-checking the
stances conveyed by their relevant microblog posts, and stances are also
conditioned on the nature of the rumor. However, most stance detection methods
require enormous post-level stance labels for training, which are
labor-intensive given a large number of posts. Enlightened by Multiple Instance
Learning (MIL) scheme, we first represent the diffusion of claims with
bottom-up and top-down trees, then propose two tree-structured weakly
supervised frameworks to jointly classify rumors and stances, where only the
bag-level labels concerning claim's veracity are needed. Specifically, we
convert the multi-class problem into a multiple MIL-based binary classification
problem where each binary model focuses on differentiating a target stance or
rumor type and other types. Finally, we propose a hierarchical attention
mechanism to aggregate the binary predictions, including (1) a bottom-up or
top-down tree attention layer to aggregate binary stances into binary veracity;
and (2) a discriminative attention layer to aggregate the binary class into
finer-grained classes. Extensive experiments conducted on three Twitter-based
datasets demonstrate promising performance of our model on both claim-level
rumor detection and post-level stance classification compared with
state-of-the-art methods.Comment: Accepted by SIGIR 202
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