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– Questioning Headlines Using a question as a headline creates the impression that the claim is credible, though it may not be true.
Source publication
Widespread adoption of internet technologies has changed the way that news is created and consumed. The current online news environment is one that incentivizes speed and spectacle in reporting, at the cost of fact-checking and verification. The line between user generated content and traditional news has also become increasingly blurred. This post...
Context in source publication
Context 1
... fact (Silverman, 2015 p.8). Two examples of this are headline/body dissonance and "questioning" headlines. The first refers to the use of headlines that introduce an unverified story as if it were fact (see Fig. 1), while the second refers to headlines phrased as a question to lend credence to an extraordinary or unsubstantiated claim (see Fig. 2). Since 55% of readers who click on an article link don't end up reading the actual article (Haile, 2014), these sorts of headlines can be considered misleading "clickbait" 1 and play a significant role in spreading misinformation online (for further discussion of fake news, see ...
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Citations
... Second, since titles are designed to contain the most essential information (the gist) related to the content and encapsulate the most important keywords that are integral to disclosing the content of the text (Soler, 2007), their language tends to contain indicative descriptive linguistic environment, which is integral to this study. Third, seeing that titles are often formulated so as to attract the readers' attention (Chen et al., 2015;Ellis, 2001;Saxena, 2006), they tend to build on impactful constructions and patterns that can be analysed to detect the underlying attitudes attributed to buzzwords by the authors compiling the titles. ...
... Conroy et al. (2015) have asserted that crossover approaches that join dialectal signal and ML with network-based social information can be used to evaluate the veracity of an assertion. Chen et al. (2015b) in another work asserted that there is a requirement of a computerized assistive tool to help content inventors as well as clients in gauging the trustworthiness of online newscasts. Zhao et al. (2015) designed a rumour detection approach based on a detector that searches for the enquiry patterns or signal tweets, clustering together of related posts and ranking of those clusters based on statistical features of truly containing a disputed authentic claim. ...
Purpose
Owing to the increased accessibility of internet and related technologies, more and more individuals across the globe now turn to social media for their daily dose of news rather than traditional news outlets. With the global nature of social media and hardly any checks in place on posting of content, exponential increase in spread of fake news is easy. Businesses propagate fake news to improve their economic standing and influencing consumers and demand, and individuals spread fake news for personal gains like popularity and life goals. The content of fake news is diverse in terms of topics, styles and media platforms, and fake news attempts to distort truth with diverse linguistic styles while simultaneously mocking true news. All these factors together make fake news detection an arduous task. This work tried to check the spread of disinformation on Twitter.
Design/methodology/approach
This study carries out fake news detection using user characteristics and tweet textual content as features. For categorizing user characteristics, this study uses the XGBoost algorithm. To classify the tweet text, this study uses various natural language processing techniques to pre-process the tweets and then apply a hybrid convolutional neural network–recurrent neural network (CNN-RNN) and state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) transformer.
Findings
This study uses a combination of machine learning and deep learning approaches for fake news detection, namely, XGBoost, hybrid CNN-RNN and BERT. The models have also been evaluated and compared with various baseline models to show that this approach effectively tackles this problem.
Originality/value
This study proposes a novel framework that exploits news content and social contexts to learn useful representations for predicting fake news. This model is based on a transformer architecture, which facilitates representation learning from fake news data and helps detect fake news easily. This study also carries out an investigative study on the relative importance of content and social context features for the task of detecting false news and whether absence of one of these categories of features hampers the effectiveness of the resultant system. This investigation can go a long way in aiding further research on the subject and for fake news detection in the presence of extremely noisy or unusable data.
... The hypothesis led to the development of forensic tools to assess testimonies at the statement level. Some approaches operationalize deception detection at scale to detect uncertainty in social media posts, for example [242] and [54]. In this regard, [201] use rhetorical structure theory as a measure of story coherence and as an indicator for fake news. ...
With the growing importance of the World Wide Web, the major challenges our society faces are also increasingly affecting the digital areas of our lives. Some of the associated problems can be addressed by computer science, and some of these specifically by data-driven research. To do so, however, requires to solve open issues related to archive quality and the large volume and variety of the data contained. This dissertation contributes data, algorithms, and concepts towards leveraging the big data and temporal provenance capabilities of web archives to tackle societal challenges. We selected three such challenges that highlight the central issues of archive quality, data volume, and data variety, respectively: (1) For the preservation of digital culture, this thesis investigates and improves the automatic quality assurance of the web page archiving process, as well as the further processing of the resulting archive data for automatic analysis. (2) For the critical assessment of information, this thesis examines large datasets of Wikipedia and news articles and presents new methods for automatically determining quality and bias. (3) For digital security and privacy, this thesis exploits the variety of content on the web to quantify the security of mnemonic passwords and analyzes the privacy-aware re-finding of the various seen content through private web archives.
... In order to identify the environment factors observed by hotel users, the results of the textual analysis were analyzed for each of the nodes [72]. ...
Excessive internet gaming in high prevalence is a very common problem that has been increasing in recent years, especially in teenagers and university students. However, there is a lack of psychometric evaluation for Internet Gaming Disorder in the Latin American context, particularly in Ecuador. This paper aims to examine the psychometric properties of the Spanish version of the Internet Gaming Disorder test (IGD-20 test) in university and high school students (n=2931, Mage=15.99, 57.22% male and 42.51% female). The validation process was performed using one, two, five and six factors taking into account the Spanish, Chinese, Korean, Arabic and Turkish contexts. After checking the models proposed to date, the best fit model was the one with a single factor. Using two samples according to gender (male, female), the invariance has been confirmed with an excellent internal consistency. All, α=0.94; Male, α=0.93; Female, α=0.93). Furthermore, we performed correlation analyses between the IGD-20 Test and socio-demographic variables, and finally, the IGD-20 Test applied to Ecuadorian teenagers and young people demonstrated good psychometric properties.
... Regarding misleading headlines, this type of headlines tend to be more focused on attracting the reader's attention -with little regard for accuracy-thus leading to misor disinformation through erroneous/false facts or headline/body text dissonance [13]. ...
This paper presents an exploratory study that analyzes the benefits and drawbacks of summarization techniques for the headline stance detection. Different types of summarization approaches are tested, as well as two stance detection methods (machine learning vs deep learning) on two state-of-the-art datasets (Emergent and FNC–1). Journalists’ headlines sourced from the Emergent dataset have demonstrated with very competitive results that they can be considered a summary of the news article. Based on this finding, this work evaluates the effectiveness of using summaries as a substitute for the full body text to determine the stance of a headline. As for automatic summarization methods, although there is still some room for improvement, several of the techniques analyzed show greater results compared to using the full body text—Lead Summarizer and PLM Summarizer are among the best-performing ones. In particular, PLM summarizer, especially when five sentences are selected as the summary length and deep learning is used, obtains the highest results compared to the other automatic summarization methods analyzed.
... Unfortunately, in practice, headlines tend to be more focused on attracting the reader's attention and going viral because of this, despite the lack of veracity within the information in the body text, thus leading to mis-or disinformation through erroneous/false facts or headline/body dissonance [6]. Headlines are considered misleading or incongruent when they significantly misrepresent the findings reported in the news article [7], by exaggerating or distorting the facts described in the news article. ...
The spread of fake news and misinformation is causing serious problems to society, partly due to the fact that more and more people only read headlines or highlights of news assuming that everything is reliable, instead of carefully analysing whether it can contain distorted or false information. Specifically, the headline of a correctly designed news item must correspond to a summary of the main information of that news item. Unfortunately, this is not always happening, since various interests, such as increasing the number of clicks as well as political interests can be behind of the generation of a headlines that does not meet its intended original purpose. This paper analyses the use of automatic news summaries to determine the stance (i.e., position) of a headline with respect to the body of text associated with it. To this end, we propose a two-stage approach that uses summary techniques as input for both classifiers instead of the full text of the news body, thus reducing the amount of information that must be processed while maintaining the important information. The experimentation has been carried out using the Fake News Challenge FNC-1 dataset, leading to a 94.13% accuracy, surpassing the state of the art. It is especially remarkable that the proposed approach, which uses only the relevant information provided by the automatic summaries instead of the full text, is able to classify the different stance categories with very competitive results, so it can be concluded that the use of the automatic extractive summaries has a positive impact for determining the stance of very short information (i.e., headline, sentence) with respect to its whole content.
... We consider higher informative saturation an exceedingly valuable feature of titles, for it fuels a more prolific use of pre-and post-modifiers that are essential to the present study as we focus on the co-occurring elements accompanying the term "buzzword". Lastly, both research papers and newspaper articles tend to summarise the key ideas of the narrative synoptically in their headings to attract the readers' attention (Chen et al., 2015;Bowles and Borden 2000;Ellis, 2001;Saxena, 2006). This prompts the authors to formulate their titles in the most impactful and conclusive way, which, among other things, entails using impactful and conclusive language in their titles. ...
The study argues that while the existing research on buzzwords mainly involves the functional and pragmatic analysis of their individual specimen, such as “engagement”, “synergy” or “development”, an alternative approach can be implemented to look into the nature and immediate implications behind the term in question. The suggested approach involves investigating the occurrences of the term proper as opposed to analysing individual buzzwords. The authors hypothise and demonstrate that linguistic context of the term “buzzword”, i.e. the peculiarities of its occurrence alongside different language units and patterns, may provide credible inferences concerning the key properties attached to the term and afford some illuminating perspective on the prevailing attitudes towards it. The study uses titles of research and newspaper articles retrieved from Google Scholar as material for the research due to better representation of the term in titles as opposed to full texts, as well as titles’ higher informativity and better descriptive saturation. Apart from the prevailing focus on the immediate linguistic context surrounding the term, the inferences made in this study also stem from the frequency data showing how often a certain feature of buzzwords is being mentioned in the titles. The study showed that while the greatest emphasis is being placed on the field of use and temporal lifecycle of buzzwords, the attitudes towards them can be best of all described by examining the place of the term in oppositions. As illustrated in the paper, these oppositions reflect the idea of the term’s inferior status by opposing it to more favourable concepts.
... It finds whether given information is credible and its probability. Chen.Y et al [5] on the other hand used news items and investigated on the need for an approach for automatic crap detector.Pogue, D et.al [6] illustrated mechanisms useful for stamping out fake news so as to filter them. Konagalaet.al [7] used deep learning methods use supervised learning approach for fake news detection. ...
... It finds whether given information is credible and its probability. Chen.Y et al [5] on the other hand used news items and investigated on the need for an approach for automatic crap detector.Pogue, D et.al [6] illustrated mechanisms useful for stamping out fake news so as to filter them. Konagalaet.al [7] used deep learning methods use supervised learning approach for fake news detection. ...
With the proliferation of social media, news is spreading faster. Moreover, social feedback became essential for organizations and governments to grow in intelligence and make strategies. However, there are cases of fake news that cause potential issues as they have wherewithal to influence people. The news in social media are polluted with fake news items if they are not identified correctly and removed from time to time. It is a challenging problem to be addressed. Many existing algorithms to detect fake news were good in performance. However, there is need for utilizing advanced Artificial Intelligence in the form of deep learning to leverage the state of the art. Towards this end, in this paper, an algorithm is designed to realize a fake news detection framework. The algorithm is known as Deep CNN based Fake New Detection (DCNN-FND) which exploits deep Convolutional Neural Network (CNN) and novel pre-processing mechanism with Natural Language Processing (NLP). The proposed framework implemented using Python data science platform, evaluated for its performance and compared with many existing techniques. The results revealed that the DCNN-FND shows better performance over the state of the art.
... Clickbait headlines have becoming more prominent since the advertiser decide to count popularity based on clicks. Some news that were deemed as less newsworthy used clickbait to attract readers, to ensure that the news still makes money (Chen et al., 2015b). Since then, a lot of scientific articles proposed ways to detect clickbait using Artificial Intelligence, one of them was fine tuned for Indonesian headlines (Chakraborty et al., 2016;Chen et al., 2015b;Anand et al., 2017;Agrawal, 2016;Biyani et al., 2016;Maulidi et al., 2018;Fakhruzzaman et al., 2021). ...
... Some news that were deemed as less newsworthy used clickbait to attract readers, to ensure that the news still makes money (Chen et al., 2015b). Since then, a lot of scientific articles proposed ways to detect clickbait using Artificial Intelligence, one of them was fine tuned for Indonesian headlines (Chakraborty et al., 2016;Chen et al., 2015b;Anand et al., 2017;Agrawal, 2016;Biyani et al., 2016;Maulidi et al., 2018;Fakhruzzaman et al., 2021). However, those articles often propose methods only but did not create any usable tool for the readers. ...
With increasing usage of clickbaits in Indonesian Online News, newsworthy articles sometimes get buried among clickbaity news. A reliable and lightweight tool is needed to detect such clickbaits on-the-go. Leveraging state-of-the-art natural language processing model BERT, a RESTful API based application is developed. This study offloaded the computing resources needed to train the model on the cloud server, while the client-side application only needs to send a request to the API and the cloud server will handle the rest. This study proposed the design and developed a web-based application to detect clickbait in Indonesian using IndoBERT as a language model. The application usage is discussed and available for public use with a performance of mean ROC-AUC of 89%.
