A corpus of Hindi news headlines shared on Twitter was created by collecting tweets of 5 mainstream Hindi news sources for a period of 4 months. 7 independent annotators were recruited to mark the 20 most retweeted news posts by each of the 5 news sources on its clickbait nature. The clickbait score hence generated was assessed for its correlation with interactions on the platform (retweets, favorites, reader replies), tweet word count, and normalized POS (part-of-speech) tag counts in tweets. A positive correlation was observed between readers' interactions with tweets and tweets' clickbait score. Significant correlations were also observed for POS tag counts and clickbait score. The prevalence of clickbait in mainstream Hindi news media was found to be similar to its prevalence in English news media. We hope that our observations would provide a platform for discussions on clickbait in mainstream Hindi news media.
Online news media sometimes use misleading headlines to lure users to open the news article. These catchy headlines that attract users but disappointed them at the end, are called Clickbaits. Because of the importance of automatic clickbait detection in online medias, lots of machine learning methods were proposed and employed to find the clickbait headlines. In this research, a model using deep learning methods is proposed to find the clickbaits in Clickbait Challenge 2017's dataset. The proposed model gained the first rank in the Clickbait Challenge 2017 in terms of Mean Squared Error. Also,…
In this paper, we present a novel hostility detection dataset in Hindi language. We collect and manually annotate ~8200 online posts. The annotated dataset covers four hostility dimensions: fake news, hate speech, offensive, and defamation posts, along with a non-hostile label. The hostile posts are also considered for multi-label tags due to a significant overlap among the hostile classes. We release this dataset as part of the CONSTRAINT-2021 shared task on hostile post detection.
Click counts are related to the amount of money that online advertisers paid to news sites. Such business models forced some news sites to employ a dirty trick of click-baiting, i.e., using a hyperbolic and interesting words, sometimes unfinished sentence in a headline to purposefully tease the readers. Some Indonesian online news sites also joined the party of clickbait, which indirectly degrade other established news sites' credibility. A neural network with a pre-trained language model M-BERT that acted as a embedding layer is then combined with a 100 nodes hidden layer and topped with a si…
Universitas Airlangga（艾尔朗加大学）；Faculty of Advanced Technology and Multidiscipline（先进技术与多学科学院）
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…
Prompt-tuning for Clickbait Detection via Text Summarization
School of Information Engineering, Yangzhou University（扬州大学信息工程学院）；School of Electronics and Information Engineering, Anhui Jianzhu University（安徽建筑大学电子与信息工程学院）
National Institute of Technology Raipur（印度劳普尔国家技术学院）；Bauhaus Universitat Weimar（魏玛包豪斯大学）
Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempted visitors to click on a particular link either to monetize the landing page or to spread the false news for sensationalization. The presence of clickbaits on any news aggregator portal may lead to unpleasant experience to readers. Automatic detection of clickbait headlines from news headlines has been a challenging issue for the machine learning community. A lot of methods have been proposed for preventing clickbait articles i…
The parallel corpus for multilingual NLP tasks, deep learning applications like Statistical Machine Translation Systems is very important. The parallel corpus of Hindi-English language pair available for news translation task till date is of very limited size as per the requirement of the systems are concerned. In this work we have developed an automatic parallel corpus generation system prototype, which creates Hindi-English parallel corpus for news translation task. Further to verify the quality of generated parallel corpus we have experimented by taking various performance metrics and the r…
Centre for Research and Technology Hellas - Information Technologies Institute（希腊研究与技术中心 - 信息技术研究所）
The emergence of social media as news sources has led to the rise of clickbait posts attempting to attract users to click on article links without informing them on the actual article content. This paper presents our efforts to create a clickbait detector inspired by fake news detection algorithms, and our submission to the Clickbait Challenge 2017. The detector is based almost exclusively on text-based features taken from previous work on clickbait detection, our own work on fake post detection, and features we designed specifically for the challenge. We use a two-level classification approac…
LLM-guided headline rewriting for clickability enhancement without clickbait
Intelligent Systems, Afeka Academic College of Engineering（阿菲卡工程学院智能系统系）；School of Computer Science, Faculty of Sciences, HIT-Holon Institute of Technology（霍隆理工学院理学院计算机科学系）
MBZUAI, UAE（阿联酋MBZUAI）；Sofia University "St. Kliment Ohridski", Bulgaria（保加利亚索菲亚大学）；Athens University of Economics and Business, Greece（希腊雅典经济与商业大学）；University of Porto, Portugal（葡萄牙波尔图大学）；University of Helsinki, Finland（芬兰赫尔辛基大学）；Indian Institute of Technology Delhi, India（印度德里印度理工学院）；University of Padova, Italy（意大利帕多瓦大学）；European Commission Joint Research Centre, Italy（意大利欧盟联合研究中心）；Institute of Computer Science, Polish Academy of Sciences, Poland（波兰科学院计算机科学研究所）
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）
Clickbait is a pejorative term describing web content that is aimed at generating online advertising revenue, especially at the expense of quality or accuracy, relying on sensationalist headlines or eye-catching thumbnail pictures to attract click-throughs and to encourage forwarding of the material over online social networks. We use distributed word representations of the words in the title as features to identify clickbaits in online news media. We train a machine learning model using linear regression to predict the cickbait score of a given tweet. Our methods achieve an F1-score of 64.98\…
Times Internet Limited（Times Internet有限公司）；Birla Institute of Technology & Science Pilani（比尔拉理工学院皮拉尼分校）
In this era of digitisation, news reader tend to read news online. This is because, online media instantly provides access to a wide variety of content. Thus, people don't have to wait for tomorrow's newspaper to know what's happening today. Along with these virtues, online news have some vices as well. One such vice is presence of social media posts (tweets) relating to news articles whose sole purpose is to draw attention of the users rather than directing them to read the actual content. Such posts are referred to as clickbaits. The objective of this project is to develop a system which wou…
From Fragments to Facts: A Curriculum-Driven DPO Approach for Generating Hindi News Veracity Explanations
TCS Research（TCS研究）；Department of Computer Science and Engineering, Indian Institute of Technology Patna（印度理工学院帕纳布计算机科学与工程系）；Indian Institute of Technology Patna（印度理工学院帕纳布）；University of Innsbruck（因斯布鲁克大学）
PathCheck Foundation（PathCheck基金会）；Indian Institute of Information Technology, Sri City（印度信息技术学院斯里城分校）
Withtheadventofsocialmedia,therehasbeenanextremely rapid increase in the content shared online. Consequently, the propagation of fake news and hostile messages on social media platforms has also skyrocketed. In this paper, we address the problem of detecting hostile and fake content in the Devanagari (Hindi) script as a multi-class, multi-label problem. Using NLP techniques, we build a model that makes use of an abusive language detector coupled with features extracted via Hindi BERT and Hindi FastText models and metadata. Our model achieves a 0.97 F1 score on coarse grain evaluation on Hostil…
Universitat Politècnica de València（瓦伦西亚理工大学）；Autoritas Consulting（奥里塔斯咨询公司）
Fake news is risky since it has been created to manipulate the readers' opinions and beliefs. In this work, we compared the language of false news to the real one of real news from an emotional perspective, considering a set of false information types (propaganda, hoax, clickbait, and satire) from social media and online news articles sources. Our experiments showed that false information has different emotional patterns in each of its types, and emotions play a key role in deceiving the reader. Based on that, we proposed a LSTM neural network model that is emotionally-infused to detect false …
ICPR 2024 Competition on Multilingual Claim-Span Identification
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；Centre for Artificial Intelligence and Robotics, DRDO（国防研究与发展组织人工智能与机器人中心）；NSHM Institute of Computing & Analytics（NSHM计算与分析学院）
Arizona State University（亚利桑那州立大学）；The Penn State University（宾夕法尼亚州立大学）
In recent years, disinformation including fake news, has became a global phenomenon due to its explosive growth, particularly on social media. The wide spread of disinformation and fake news can cause detrimental societal effects. Despite the recent progress in detecting disinformation and fake news, it is still non-trivial due to its complexity, diversity, multi-modality, and costs of fact-checking or annotation. The goal of this chapter is to pave the way for appreciating the challenges and advancements via: (1) introducing the types of information disorder on social media and examine their …
Dept. of CSE IIT Kharagpur, India（印度 IIT 卡哈拉格普尔分校计算机科学与工程系）；Dept. of CSE IIIT Delhi, India（印度德里信息技术学院计算机科学与工程系）；IBM Research Bangalore, India（印度班加罗尔 IBM 研究院）
The sudden widespread menace created by the present global pandemic COVID-19 has had an unprecedented effect on our lives. Man-kind is going through humongous fear and dependence on social media like never before. Fear inevitably leads to panic, speculations, and the spread of misinformation. Many governments have taken measures to curb the spread of such misinformation for public well being. Besides global measures, to have effective outreach, systems for demographically local languages have an important role to play in this effort. Towards this, we propose an approach to detect fake news abo…
Chittagong University of Engineering and Technology（吉大港工程技术大学）
This paper illustrates a detail description of the system and its results that developed as a part of the participation at CONSTRAINT shared task in AAAI-2021. The shared task comprises two tasks: a) COVID19 fake news detection in English b) Hostile post detection in Hindi. Task-A is a binary classification problem with fake and real class, while task-B is a multi-label multi-class classification task with five hostile classes (i.e. defame, fake, hate, offense, non-hostile). Various techniques are used to perform the classification task, including SVM, CNN, BiLSTM, and CNN+BiLSTM with tf-idf a…
Advancing Topic Segmentation of Broadcasted Speech with Multilingual Semantic Embeddings
Fraunhofer Institute for Digital Media Technology（弗劳恩霍夫数字媒体技术研究所）；Fraunhofer Institute for Intelligent Analysis and Information Systems（弗劳恩霍夫智能分析与信息系统研究所）
