Clickbait detection on WeChat: A deep model integrating semantic and syntactic information

Liu, Tong; Yu, Ke; Wang, Lu; Zhang, Xuanyu; Zhou, Hao; Wu, Xiaofei

doi:10.1016/j.knosys.2022.108605

Cited by 10 publications

(9 citation statements)

References 17 publications

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“…The output layer consists of two nodes (0 and 1) with softmax activation units and 0.00001 L1 regularisation. Moreover, four different settings of BERT [15] was implemented to model clickbait classification from online news. The hyper-parameters of all BERT model are: batch size=32, maximum epochs=10, initial learning with early stopping and learning rate reduction applied.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Thakur does other research about clickbait detection by using deep learning [8], [9] which suggests the recurrent CNN overcomes the heavy feature engineering in clickbait detection. This method has been tested and turns out that the accuracy is better than the other clickbait-detection algorithms such as LSTM [10]- [12], CNN [8], [13], [14], BERT [15], [16], or conventional machine learning algorithms [17]- [21].…”

Section: Recent Workmentioning

confidence: 99%

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Identifying clickbait in online news using deep learning

2023

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Several industries use clickbait techniques as their strategy to increase the number of readers for their news. Some news companies implement catchy headlines and images in their news article links, with the expectation that the readers will be interested in reading the news and click the provided link. The majority of the news is not hoax news. However, the content might not be as grand as the catchy headlines and images provided to the readers. This research aims to explore the classification model using machine learning to identify if the headlines are classified as clickbait in online news. This research explores several machine learning techniques to classify clickbait in online news and comprehensively explain the results. Several popular machine learning techniques were implemented and explored in this research. The results demonstrate that the model trained with fast large margin provides the best accuracy and classification error (90% and 10%, respectively). Moreover, to improve the performance, bidirectional encoder representations from transformers architecture was used to model clickbait in online news. The best BERT model achieved 98.86% in the test accuracy. BERT model requires more time to train (0.9 hour) compared to machine learning (0.4 hour).

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Recent Workmentioning

confidence: 99%

Identifying clickbait in online news using deep learning

2023

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“…Nevertheless, GNNs have also demonstrated success in modeling textual content based on syntactical dependency graphs. These approaches typically entail using GNN-based methods, like GCN [12,35] and GAT [7,40], to encode the syntax graph predicted by off-the-shelf dependency parsers, subsequently generating textual graph embeddings tailored to specific tasks, and more recent research focuses on synergizing semantic and syntactical components to complement semantic information [17,18,34,46]. However, GNN-based approaches face limitations.…”

Section: Graph Neural Networkmentioning

confidence: 99%

“…Attention-based [20,26,36,41,51] methods, which are particularly popular, utilizing attention mechanism [39] to capture relations within or between text from a global perspective. GNN-based methods focus on textual graph construction within documents [38] or the syntactical dependency relation between words [18,34]. Additionally, methods using external factual verification [15,48,53] contribute to enhanced detection performance.…”

Section: Related Work 21 Fake News Detectionmentioning

confidence: 99%

MSynFD: Multi-hop Syntax Aware Fake News Detection

Xiao,

Zhang,

Shi

et al. 2024

Proceedings of the ACM Web Conference 2024

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The proliferation of social media platforms has fueled the rapid dissemination of fake news, posing threats to our real-life society. Existing methods use multimodal data or contextual information to enhance the detection of fake news by analyzing news content and/or its social context. However, these methods often overlook essential textual news content (articles) and heavily rely on sequential modeling and global attention to extract semantic information. These existing methods fail to handle the complex, subtle twists 1 in news articles, such as syntax-semantics mismatches and prior biases, leading to lower performance and potential failure when modalities or social context are missing. To bridge these significant gaps, we propose a novel multi-hop syntax aware fake news detection (MSynFD) method, which incorporates complementary syntax information to deal with subtle twists in fake news. Specifically, we introduce a syntactical dependency graph and design a multi-hop subgraph aggregation mechanism to capture multi-hop syntax. It extends the effect of word perception, leading to effective noise filtering and adjacent relation enhancement. Subsequently, a sequential relative position-aware Transformer is designed to capture the sequential information, together with an elaborate keyword debiasing module to mitigate the prior bias. Extensive experimental results on two public benchmark datasets verify the effectiveness and superior performance of our proposed MSynFD over state-of-the-art detection models. CCS CONCEPTS• Computing methodologies → Artificial intelligence.

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“…Therefore, the development of tools to detect clickbait automatically has occurred. Detecting clickbait is essential to maintaining the media's credibility (Liu et al , 2022). Clickbait detection has thus become an emerging research field.…”

Section: Introductionmentioning

confidence: 99%

CA-CD: context-aware clickbait detection using new Chinese clickbait dataset with transfer learning method

Wang,

Maslim,

Liu

2023

DTA

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PurposeA clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.Design/methodology/approachThis study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.FindingsThis research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.Originality/valueThe originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.

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Clickbait detection on WeChat: A deep model integrating semantic and syntactic information

Cited by 10 publications

References 17 publications

Identifying clickbait in online news using deep learning

Identifying clickbait in online news using deep learning

MSynFD: Multi-hop Syntax Aware Fake News Detection

CA-CD: context-aware clickbait detection using new Chinese clickbait dataset with transfer learning method

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