Applying Automatic Text Summarization for Fake News Detection
15571Authors
nan
Abstract
AbstractThe distribution of fake news is not a new but a rapidly growing problem. The shift to news consumption via social media has been one of the drivers for the spread of misleading and deliberately wrong information, as in addition to its ease of use there is rarely any veracity monitoring. Due to the harmful effects of such fake news on society, the detection of these has become increasingly important. We present an approach to the problem that combines the power of transformer-based language models while simultaneously addressing one of their inherent problems. Our framework, CMTR-BERT, combines multiple text representations, with the goal of circumventing sequential limits and related loss of information the underlying transformer architecture typically suffers from. Additionally, it enables the incorporation of contextual information. Extensive experiments on two very different, publicly available datasets demonstrates that our approach is able to set new state-of-the-art performance benchmarks. Apart from the benefit of using automatic text summarization techniques we also find that the incorporation of contextual information contributes to performance gains.
Keywords
Full Content
Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 267–276 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 267 ‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models Areej Alhassan1, Jinkai Zhang2 and Viktor Schlegel 2 1King Saud University, Riyadh, Saudi Arabia 2University of Manchester, Manchester, United Kingdom aralhassan@ksu.edu.sa, jinkai.zhang@student.manchester.ac.uk, viktor.schlegel@manchester.ac.uk Abstract Natural language processing (NLP) has been shown to perform well in various tasks, such as answering questions, ascertaining natural language inference and anomaly detection. However, there are few NLP-related studies that touch upon the moral context conveyed in text. This paper studies whether state-of-the-art, pre-trained language models are capable of passing moral judgments on posts retrieved from a popular Reddit user board. Reddit is a social discussion website and forum where posts are promoted by users through a voting system. In this work, we construct a dataset that can be used for moral judgement tasks by collecting data from the AITA? (Am I the A*******?) subreddit. To model our task, we harnessed the power of pre-trained language models, including BERT, RoBERTa, RoBERTa-large, ALBERT and Longformer. We then fine-tuned these models and evaluated their ability to predict the correct verdict as judged by users for each post in the datasets. RoBERTa showed relative improvements across the three datasets, exhibiting a rate of 87% accuracy and a Matthews correlation coefficient (MCC) of 0.76, while the use of the Longformer model slightly improved the performance when used with longer sequences, achieving 87% accuracy and 0.77 MCC. Keywords: moral judgments, AITA subreddit, pre-trained language models. 1. Introduction In some cases, people might doubt if their behaviour is in line with publicly accepted norms and customs. The increasing number of highly specialised discussion forums on the Internet opened up the opportunity for these people to hear a second opinion about whether they behaved rightly or wrongly in a certain situation. A popular example of these specialised forums is a subreddit on Reddit called AmItheA******?1 (AITA?), which is dedicated to passing moral judgement on everyday conflicts. A person can open a new thread to describe a situation that led to a conflict between multiple parties and the board community gets the chance to cast one of four different votes with regard to the described situation: not the a****** (NTA), you’re the a****** (YTA), no a****** here (NAH) and everyone sucks here (ESH). Voters can also provide an explanation describing how they came to their conclusion, and the majority of votes determines the final verdict. The discussions in this subreddit can be highly complex; for example, their complexity is reflected in the fact that the stories may involve multiple parties. Furthermore, they usually consist of multiple paragraphs. Thus, passing a rational judgement requires a deep understanding of context. Moreover, this particular type of decision-making requires the ability to link the information conveyed in the post to a specific type of background knowledge, i.e. the set of norms or customs …
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Labeled Text
AbstractThe distribution of fake news is not a new but a rapidly growing problem. The shift to news consumption via social media has been one of the drivers for the spread of misleading and deliberately wrong information, as in addition to its ease of use there is rarely any veracity monitoring. Due to the harmful effects of such fake news on society, the detection of these has become increasingly important. We present an approach to the problem that combines the power of transformer-based language models while simultaneously addressing one of their inherent problems. Our framework, CMTR-BERT, combines multiple text representations, with the goal of circumventing sequential limits and related loss of information the underlying transformer architecture typically suffers from. Additionally, it enables the incorporation of contextual information. Extensive experiments on two very different, publicly available datasets demonstrates that our approach is able to set new state-of-the-art performance benchmarks. Apart from the benefit of using automatic text summarization techniques we also find that the incorporation of contextual information contributes to performance gains.
Publication Details
- Paper ID:
- 15571
- Year:
- 2022
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan