LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification

Azevedo, Lucas; d’Aquin, Mathieu; Davis, Brian; Zarrouk, Manel

doi:10.18653/v1/2021.findings-acl.4

Cited by 6 publications

(3 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This includes using syntactic, lexical, semantic, discursive, and morphological features; but also other properties of text, such a readability, similarity, punctuation, quality, informality, subjectivity, diversity, uncertainty, complexity, specificity, sentiment, and emotions. It has been found that part-of-speech counts, lexical diversity, informality and readability are some of the most distinctive features of fake news (Azevedo, D'aquin, Davis, & Zarrouk, 2021;Castelo et al, 2019). Conversely, stylometry can be learned by disinformers in order to replicate the styles of reliable news, as shown in (Schuster, Schuster, Shah, & Barzilay, 2020), which calls for approaches that continuously update over time.…”

Section: Natural Language Processing For Stylistic Characterizationmentioning

confidence: 99%

IBERIFIER Reports: Is the ‘AI toolbox for disinformation’ ready?

Montoro Montarroso,

Camacho,

Martín

et al. 2023

View full text Add to dashboard Cite

This project has received funding from the European Commission under the call CEF-TC-2020-2 (European Digital Media Observatory)Reference: 2020-EU-IA-0252 Consortium DisclaimerAll information provided reflects the status of the IBERIFIER project at the time of writing and may be subject to change.Neither the IBERIFIER Consortium as a whole, nor any single party within the IBERIFIER Consortium warrant that the information contained in this document is capable of use, nor that the use of such information is free from risk. Neither the IBERIFIER Consortium as a whole, nor any single party within the IBERIFIER Consortium accepts any liability for loss or damage suffered by any person using the information.This document does not represent the opinion of the European Community, and the European Community is not responsible for any use that might be made of its content.

show abstract

Section: Natural Language Processing For Stylistic Characterizationmentioning

confidence: 99%

IBERIFIER Reports: Is the ‘AI toolbox for disinformation’ ready?

Montoro Montarroso,

Camacho,

Martín

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Early studies on fake news detection have attempted to exploit various approaches to extract features from news content and social context information, including linguistic features (Potthast et al, 2018;Azevedo et al, 2021), visual clues (Jin et al, 2016), temporal traits (Kwon et al, 2013Ma et al, 2015), user behaviors and profiles (Castillo et al, 2011;Ruchansky et al, 2017;Shu et al, 2019b). Subsequent studies have employed neural networks to automatically learn deep feature representations from similar sources of data (Ma et al, 2016;Popat et al, 2018;Nguyen et al, 2020;Kaliyar et al, 2021;Sheng et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom

Yang,

Gao,

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

In recent years, we witness the explosion of false and unconfirmed information (i.e., rumors) that went viral on social media and shocked the public. Rumors can trigger versatile, mostly controversial stance expressions among social media users. Rumor verification and stance detection are different yet relevant tasks. Fake news debunking primarily focuses on determining the truthfulness of news articles, which oversimplifies the issue as fake news often combines elements of both truth and falsehood. Thus, it becomes crucial to identify specific instances of misinformation within the articles. In this research, we investigate a novel task in the field of fake news debunking, which involves detecting sentence-level misinformation. One of the major challenges in this task is the absence of a training dataset with sentence-level annotations regarding veracity. Inspired by the Multiple Instance Learning (MIL) approach, we propose a model called Weakly Supervised Detection of Misinforming Sentences (WSDMS). This model only requires bag-level labels for training but is capable of inferring both sentence-level misinformation and article-level veracity, aided by relevant social media conversations that are attentively contextualized with news sentences. We evaluate WSDMS on three real-world benchmarks and demonstrate that it outperforms existing stateof-the-art baselines in debunking fake news at both the sentence and article levels.

show abstract

“…Others are created from scratch with a tempting caption to enhance website traffic and visits. Recently, there have been several works girding fake news features analysis [2,3,4,5,6,7]. A veritably complex task consists to supervise and investigate the diffusion's information sources and the nature of profit users.…”

Section: Introductionmentioning

confidence: 99%

Hybrid-MELAu: A Hybrid Mixing Engineered Linguistic Features Based on Autoencoder for Social Bot Detection.

Hamida

Refouf

Drif

et al. 2022

IJCAI

View full text Add to dashboard Cite

Social bots are defined as computer algorithms that generate massive amounts of obnoxious or meaningful information. Most bot detection methods leverage multitudinous characteristics, from network features, temporal dynamics features, activities features, and sentiment features. However, there has been fairly lower work exploring lexicon measurement and linguistic indicators to detect bots. The main purpose of this research is to recognize the social bots through their writing style. Thus, we carried out an exploratory study on the effectiveness of only a set of linguistic features (17 features) exploitable for bot detection, without the need to resort to other types of features. And we develop a novel framework in a hybrid fashion of Mixing Engineered Linguistic features based on Autoencoders (Hybrid-MELAu). The semisupervised Hybrid-MELAu framework is composed of two essential constituents: the features learner and the predictors. We establish the features learner innovated on two powerful structures: a) the first is a Deep dense Autoencoder fed by the Lexical and the Syntactic content (DALS) that represents the high order lexical and syntactic features in latent space, b) the second one is a Glove-BiLSTM autoencoder, which sculpts the semantic features; subsequently, we generate elite elements from the pre-trained encoder part from each latent space with transfer learning. We consider a sample of 1 Million from Cresci datasets to conduct our linguistic analysis comparison between the writing style of humans and bots. With this dataset, we observe that the bot's textual lexical diversity median is greater than the human one and the syntactic analysis based on speech-tagging shows a creative behavior in human writing style. Finally, we test the model's robustness on several public dataset (celebrity, pronbots-2019, and political bots). The proposed framework achieves a good accuracy of 92.22%. Overall, the results shown in this paper, and the related discussion, argue that it is possible to discern the differences between humans' and bots' writing styles based on an efficient linguistic deep framework.Povzetek: V prispevku je opisana metoda za detekcijo pogovornih asistentov (botov) na osnovi jezkovnega stila.

show abstract

LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification

Cited by 6 publications

References 51 publications

IBERIFIER Reports: Is the ‘AI toolbox for disinformation’ ready?

IBERIFIER Reports: Is the ‘AI toolbox for disinformation’ ready?

WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom

Hybrid-MELAu: A Hybrid Mixing Engineered Linguistic Features Based on Autoencoder for Social Bot Detection.

Contact Info

Product

Resources

About