Learning to Detect Incongruence in News Headline and Body Text via a Graph Neural Network

Yoon, Seunghyun; Park, Kunwoo; Lee, Minwoo; Kim, Taegyun; Cha, Meeyoung; Jung, Kyomin

doi:10.1109/access.2021.3062029

Cited by 9 publications

(12 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It seems similar to ours, but its document is made by permuting the sentence order within a document rather than another document. • congruence / incongruence: In [12], [18], [19], [37], [38], the goal is comparing relationships with different kinds of texts such as headline and body text in news articles. However, its document generating method is the same as our task.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Documents With Inconsistent Context

2022

View full text Add to dashboard Cite

Extremely large volumes of documents are available from online news platforms and social media. While the quantity of these documents have grown exponentially, the majority lack their quality, which can cause digital fatigue or promote misinformation. To this end, we propose a novel framework that can evaluate the quality of documents in terms of consistency. We model low-quality document detection as a binary classification task, which is able to measure how the documents have consistent contents. Specifically, we relax the problem by considering each sentence or paragraph as node. A given document is then considered as a network of nodes. We show how we define the supernode in a network and show how it is informative enough to predict whether the document is consistent or not. We believe this scheme can be applied to various applications including fake news detection, and document screening with qualitative evaluations. We achieve the state-of-the-art on existing tasks using the NELA17 dataset, and YH-News dataset which we release in this paper.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Sentences from the headline and corresponding body can be directly compared as in [8], [33]. Attention based models have been proposed recently [12], [18], [19], [37], [38] showing promising performance for detecting incongruent headline news. However, detecting incongruent headline news is only one field of interest.…”

Section: Introductionmentioning

confidence: 99%

Detecting Documents With Inconsistent Context

2022

View full text Add to dashboard Cite

show abstract

“…A hierarchical architecture that models a complex textual representation of news articles, and measures the incongruity between news headlines and body text approach is proposed in (Yoon et al, 2019). An approach to detect incongruity between a news headline and body text of a news article using a graph-based hierarchical dual encoder (GHDE) is the work of (Yoon et al, 2021). A deep hierarchical attention network that trained to extract hidden patterns in fake news using the concatenation of news headlines and their corresponding body text as input data-set is proposed in (Meel and Vishwakarma, 2021).…”

Section: Artificial Intelligence Approachesmentioning

confidence: 99%

Assessing the Inconsistency in Online News

2021

Proceedings of the International Conferences on WWW/Internet 2021 and Applied Computing 2021

View full text Add to dashboard Cite

The information on the web can be inconsistent across different web pages. News articles are examples of information on the web that are inconsistent and this paper proposes an approach that enables the visual analysis of inconsistencies in online news. It presents an approach which will enable the visual identification of inconsistencies associated to a news headline of interest. It uses a visual assessment approach that relies on two techniques, namely Fault Tolerance and Co-occurrence techniques. The Fault Tolerance technique is used in extracting related news headlines on the internet while the Co-occurrence technique is used for grouping and scaling related news headlines on the web. Also the bar-chart is used to plot charts that summaries the inconsistencies from which news readers can visually assess related news headlines of particular context.

show abstract

“…Therefore, detecting incongruent news is vital to fight social media misinformation. Researchers have currently exploited different methods for detecting fake news, ranging from simple n-gram features based methods [4], hierarchical encoding based models [5], summarization based models [6] to artificially intelligent systems [7]- [9]. Normally, a system based on artificial intelligence encounters a bottleneck when optimization and tuning of different parameters [10] are essential.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Training for Fake News Classification

et al. 2022

View full text Add to dashboard Cite

News is a source of information to know about progress in the various areas of life all across the globe. However, the volume of this information is high, and getting benefits from the available information becomes difficult. Moreover, the frequency of fake news is increasing significantly and used to fulfill a particular agenda. This led to research on the classification of news to prevent the spread of disinformation. In this work, we use Adversarial Training as a means of regularization for fake news classification. We train two transformed-based encoder models using adversarial examples that help the model learn noise invariant representations. We generate these examples by perturbing the model's word embedding matrix, and then we fine-tune the model on clean and adversarial examples simultaneously. We train and evaluate the models on the Buzzfeed Political News and Random Political News datasets. Results show consistent improvements over the baseline models when we train models using adversarial examples. Experiments show that Adversarial Training improves the performance by 1.25% over the BERT baseline, 2.05% over the Longformer baseline for the Random Political News dataset, 1.25% over the BERT baseline and 0.9% over the Longformer baseline for Buzzfeed Political News dataset in terms of F1-score.

show abstract

Learning to Detect Incongruence in News Headline and Body Text via a Graph Neural Network

Cited by 9 publications

References 29 publications

Detecting Documents With Inconsistent Context

Detecting Documents With Inconsistent Context

Assessing the Inconsistency in Online News

Adversarial Training for Fake News Classification

Contact Info

Product

Resources

About