Learning to Identify Ambiguous and Misleading News Headlines

Accuracy is one of the basic principles of journalism. However, it is increasingly hard to manage due to the diversity of news media. Some editors of online news tend to use catchy headlines which trick readers into clicking. These headlines are either ambiguous or misleading, degrading the reading experience of the audience. Thus, identifying inaccurate news headlines is a task worth studying. Previous work names these headlines "clickbaits" and mainly focus on the features extracted from the headlines, which limits the performance since the consistency between headlines and news bodies is underappreciated. In this paper, we clearly redefine the problem and identify ambiguous and misleading headlines separately. We utilize class sequential rules to exploit structure information when detecting ambiguous headlines. For the identification of misleading headlines, we extract features based on the congruence between headlines and bodies. To make use of the large unlabeled data set, we apply a co-training method and gain an increase in performance. The experiment results show the effectiveness of our methods. Then we use our classifiers to detect inaccurate headlines crawled from different sources and conduct a data analysis.Comment: Accepted by IJCAI 201

Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines

Polarization and the marketplace for impressions have conspired to make navigating information online difficult for users, and while there has been a significant effort to detect false or misleading text, multimodal datasets have received considerably less attention. To complement existing resources, we present multimodal Video Misleading Headline (VMH), a dataset that consists of videos and whether annotators believe the headline is representative of the video's contents. After collecting and annotating this dataset, we analyze multimodal baselines for detecting misleading headlines. Our annotation process also focuses on why annotators view a video as misleading, allowing us to better understand the interplay of annotators' background and the content of the videos.Comment: EMNLP 2023 Main Pape

Detecting Misleading Headlines Through the Automatic Recognition of Contradiction in Spanish

Misleading headlines are part of the disinformation problem. Headlines should give a concise summary of the news story helping the reader to decide whether to read the body text of the article, which is why headline accuracy is a crucial element of a news story. This work focuses on detecting misleading headlines through the automatic identification of contradiction between the headline and body text of a news item. When the contradiction is detected, the reader is alerted to the lack of precision or trustworthiness of the headline in relation to the body text. To facilitate the automatic detection of misleading headlines, a new Spanish dataset is created (ES_Headline_Contradiction) for the purpose of identifying contradictory information between a headline and its body text. This dataset annotates the semantic relationship between headlines and body text by categorising the relation between texts as compatible , contradictory and unrelated . Furthermore, another novel aspect of this dataset is that it distinguishes between different types of contradictions, thereby enabling a more fine-grain identification of them. The dataset was built via a novel semi-automatic methodology, which resulted in a more cost-efficient development process. The results of the experiments show that pre-trained language models can be fine-tuned with this dataset, producing very encouraging results for detecting incongruency or non-relation between headline and body text.This research work is funded by MCIN/AEI/ 10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union” or by the “European Union NextGenerationEU/PRTR” through the project TRIVIAL: Technological Resources for Intelligent VIral AnaLysis through NLP (PID2021-122263OB-C22) and the project SOCIALTRUST: Assessing trustworthiness in digital media (PDC2022-133146-C22). Also funded by Generalitat Valenciana through the project NL4DISMIS: Natural Language Technologies for dealing with dis- and misinformation (CIPROM/2021/21), and the grant ACIF/2020/177

The Truth Still Matters: Teaching Information Literacy to Combat Fake News and Alternative Facts

As the way we consume information has changed over the last two decades, so too have methods of deception and misinformation. It is clear that unlimited access to information has been equal parts enlightening and confusing as determining the truth becomes increasingly difficult. Despite popular wisdom which suggests that today’s young people are inherently skilled at assessing the credibility of online sources, evidence has shown that this could not be further from the truth. We can no longer view information literacy as merely a helpful skill for writing essays now that fake news and misinformation have proven their powers repeatedly on the world stage. It is up to educators to take a stand against misinformation by ensuring that each and every student leaves their classroom with the information literacy skills necessary for safety and success in the modern world

STRUCTURAL AMBIGUITY ON BBC NEWS INSTAGRAM POST

The arrangement of words in a grammatically correct sentence can sometimes still lead to multiple interpretations. BBC News, in its Instagram posts, contains sentences that are structurally ambiguous. Therefore, this study aims to find out types of structural ambiguity on BBC News’ Instagram posts that are published from February 2021 until July 2022 and the interpretations arise from those ambiguities. This study uses qualitative method with document analysis technique. The analysis is done by some steps: investigating which word or phrase that makes the sentences become ambiguous, categorizing the ambiguities to their each types, finding out meanings that are produced by those ambiguities, parsing the sentences by using tree diagram, and interpreting the actual meaning of the news headlines or sentences by relating it to the context. The result shows that there are fifty posts from BBC News that contain sentences which are structurally ambiguous. Using theory of Hirst, those ambiguities are categorized as attachment and analytical ambiguity. The ambiguities are mostly caused by phrases that are unclear what their functions are in a sentence. From the result, it can be seen that structural ambiguity is a language phenomenon that is still widely found in our surrounding, not to mention the news headlines. Keywords: structural ambiguity; BBC News; Instagram; headline; sentenc

Millennial students’ metalinguistic knowledge on headlines Using Grammaticality Judgment Test

Amid the proliferation of online news portals, there is a felt need for a reinvestigation of the millennial students’ metalinguistic knowledge (MK) on the technicality of the headlines. Couched within Relevance Theory, including the interlarding theories of Communicative Competence and Monitor Hypothesis, this study investigated 80 students’ technical knowledge on selected 35 headlines vis-à-vis the students’ academic disciplines and exposure to the headlines/news articles. The study employed a Grammaticality Judgment Test (GJT) by Schütze (1996) following Noam Chomsky’s competence/performance distinction. The results showed the dearth of the students’ knowledge on the technical rules of the headlines, which only fared around 70.66% accuracy. Likewise, the results showed that those who were never exposed to the headlines had a significantly lower mean score as compared to those with exposure to the headlines. Poor cognizance of the semantics-syntax of the headlines statistically cuts across eight academic disciplines and exposure to news articles. Overall, the students’ understanding of the headlines seems to be shaped by their explicit knowledge and grammaticality judgment about the technicalities of the headlines. By and large, such results may be an indication of the students’ experiences of semantic ambiguities of the headlines. We put forth the dire need for the re-introduction of ‘Journalism’ course across educational levels in a language classroom given today’s rapid pervasiveness and breadth of digitalism. Students’ lack of MK on the technicality of the headlines can impinge on their understanding of the semantics and the actual story-level depictions of the news

An Ensemble Classification and Hybrid Feature Selection Approach for Fake News Stance Detection

The developments in Internet and notions of social media have revolutionised representations and disseminations of news. News spreads quickly while costing less in social media. Amidst these quick distributions, dangerous or seductive information like user generated false news also spread equally. on social media. Distinguishing true incidents from false news strips create key challenges. Prior to sending the feature vectors to the classifier, it was suggested in this study effort to use dimensionality reduction approaches to do so. These methods would not significantly affect the result, though. Furthermore, utilising dimensionality reduction techniques significantly reduces the time needed to complete a forecast. This paper presents a hybrid feature selection method to overcome the above mentioned issues. The classifications of fake news are based on ensembles which identify connections between stories and headlines of news items. Initially, data is pre-processed to transform unstructured data into structures for ease of processing. In the second step, unidentified qualities of false news from diverse connections amongst news articles are extracted utilising PCA (Principal Component Analysis). For the feature reduction procedure, the third step uses FPSO (Fuzzy Particle Swarm Optimization) to select features. To efficiently understand how news items are represented and spot bogus news, this study creates ELMs (Ensemble Learning Models). This study obtained a dataset from Kaggle to create the reasoning. In this study, four assessment metrics have been used to evaluate performances of classifying models