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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.  

– Questioning Headlines Using a question as a headline creates the impression that the claim is credible, though it may not be true.  

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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...

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... 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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... 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. ...
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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.
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... 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]. ...
Chapter
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... 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. ...
Chapter
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. ...
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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. ...
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... 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. ...
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