China
Africa
Explore chapters and articles related to this topic
Fake News Detection Using Machine Learning
Published in Loveleen Gaur, DeepFakes, 2023
Sonali Raturi, Amit Kumar Mishra, Srabanti Maji
The work to classify fake news manually need in-depth knowledge of expertise and domain to recognize irregularities in the text. In this research, we generate a model that detects fakes news with the use of NLP and Passive-Aggressive classifier. The data we used in our work is collected through the Kaggle. The research is to identify patterns in text that differentiate fake news from real news. We pulled out different data cleaning using Natural Language Processing and Passive-Aggressive classifier for classification with an accuracy of 95%. False news detection has many issues that need the awareness of researchers. For instance, we need to recognize the key elements to reduce the spread of fake news. ML methods can be used to define the elements involved in the growth of false news. In addition, false news detection with deep learning can be another future direction using various feature extraction.
The spectacle of protest: The case of Budapest University of Theatre and Film Arts (SzFE)
Published in Mário S. Ming Kong, Maria do Rosário Monteiro, Maria João Pereira Neto, Creating Through Mind and Emotions, 2022
We are no longer merely the victims of the domination of images but also its active creators. This new development has fundamentally changed the nature of the spectacle. Debord could still speak of the “monopoly of appearance” (Debord, 1977, par. 12), interpreting the spectacle as a concentrated tool of power, which is “the total justification of the existing system’s conditions and goals”(Debord, 1977, par. 6). However, this required state television, radio, and the one-way communication of advertising. By now, the monolithic nature of the spectacle ‒ resulting from the fact that in the 1960s, the monopoly on the production and transmission of images was in the hands of a few central institutions ‒ has disappeared. The most significant change is not so much the proliferation of TV channels, radio stations, the advent of cable TV or the internet, but an opportunity that is primarily tied to the spread of smartphones and social media, making each user a potential source of news, whose content can reach any part of the world. Thus, even if there is a central news channel or, in some countries (such as Hungary), an attempt to centralize the media as a whole, the competition from alternative, personal news must be reckoned with. Thus, the instrument of the spectacle, the image, can also be an instrument of the individual or groups people, and it can be turned against the central power.
Social Theory and Networks
Published in Michael Muhlmeyer, Shaurya Agarwal, Information Spread in a Social Media Age, 2021
Michael Muhlmeyer, Shaurya Agarwal
With the evolution of information spread medium, the theories that attempt to describe and examine person-to-person communication must also evolve. Some contend that the “media is the message”. That is, the medium by which information is communicated within society has a much greater influence on a group than the specific content of the message being communicated [24]. With a decrease in the importance of traditional newspapers, television, and word-of-mouth spread, internet news, blogs, and social media have emerged as a vital vehicle for news spread. At first glance, it is easy to agree with this line of thinking. The advent of the internet has changed our lives and how we get information, yet it has had little effect on the information itself in most cases.
Social Media Users’ Opinions on Remote Work during the COVID-19 Pandemic. Thematic and Sentiment Analysis
Published in Information Systems Management, 2020
Stanisław Wrycza, Jacek Maślankowski
In general, most of the social media analytics methods are related to Machine Learning (computational intelligence), including sentiment analysis and text mining. However, current text analytics tools mostly focus only on the semantics of language. Because of this fact, new emerging methods are introduced to more complex and reliable analyses of the text, e.g., LAP – language-action perspective, to detect not only what people say but what they do with the language (Abbasi et al., 2018). Another aspect is to detect fake news, which can be an issue when discussing COVID-19 on social media. There are different fact-checking tools used to check if the news is real or fake. They usually use reliable news sources to confirm the real facts from the message (Moravec et al., 2019).
Trends in combating fake news on social media – a survey
Published in Journal of Information and Telecommunication, 2021
Botambu Collins, Dinh Tuyen Hoang, Ngoc Thanh Nguyen, Dosam Hwang
The debate on fake news detection has been a challenging one due to the complex and dynamic nature of fake news. In this paper, we did an overview of fake news detection models taking into cognizance the various types of fake news. It is a reality that fake news has caused enormous damage not only to democracy but to the freedom of speech due to its rapid spread on social media and hence detecting them become imperative. We recommend that fake news can be verified based on source, author or publishers and experts can be able to distinguish between those genuine sources and fake sources. For instance, the Macedonian teenagers that are well known for manufacturing fake news can be identified on their clickbait sites and such site removed from the network.
Can warnings curb the spread of fake news? The interplay between warning, trust and confirmation bias
Published in Behaviour & Information Technology, 2022
Kholekile L. Gwebu, Jing Wang, Ermira Zifla
In recent years, the same technologies (e.g. internet and social media platforms) that have enabled the democratisation of journalism have also become the inexpensive and easy conduit for creating, publishing, and disseminating fake news (Lazer et al. 2018; Moravec, Minas, and Dennis 2019; Vosoughi, Roy, and Aral 2018). Fake news is defined as ‘fabricated information that mimics news media content in form but not in organizational process or intent’ … ‘for ensuring the accuracy and credibility of information’ (Lazer et al. 2018,1094). Today, as a major news source for the vast majority of adults in the US, social media puts the responsibility for the credibility control of news on the public, who are generally neither trained nor accustomed to validating the news before reading or sharing them (Kim and Dennis 2019). Worse still, the news feed feature on social media automatically feeds users a mix of news from friends, past activities and paying advertisers, giving them little ability to choose their trusted news sources (Kim and Dennis 2019). Researchers find that fake news on social media spreads more rapidly and broadly than true news on all categories of information (Vosoughi, Roy, and Aral 2018), adding to a number of growing concerns about the negative effects of social media use, also known as ‘the dark side’ of social media (Talwar et al. 2019, 2020). In response to the widespread concerns about the peer-to-peer spread of fake news, in 2016, social media platforms such as Facebook and Twitter incorporated warnings into their algorithm to flag the source or news credibility (Lazer et al. 2018). These warnings contain labels such as ‘Misleading Information’, ‘Disputed Claim’ or ‘Unverified Claim’, and may also contain links to external sources that provide more information (Facebook n.d.; Roth and Pickles 2020).