Academic literature on the topic 'Fake News detection'
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Journal articles on the topic "Fake News detection"
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Kumar, Aniket, Saurabh Kumar Pal, Kumar Dhruv Roy, and Mr Ragunthar T. "Fake News Detection." International Journal of Scientific & Engineering Research 11, no. 12 (December 25, 2020): 575–80. http://dx.doi.org/10.14299/ijser.2020.12.09.
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Now-a-days it's exceedingly common in this digital world that someone for his or her benefit try to manipulate a mass with false information. With the massive use of social media by the population which is beneficial for the users most of the time, can also be used as a really good platform to spread a fake news and at worse try to create chaos in society. Fake death news of celebrities, fake news regarding wars and fake news related to politics are the day-to-day life examples.
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Chu, Samuel Kai Wah, Runbin Xie, and Yanshu Wang. "Cross-Language Fake News Detection." Data and Information Management 5, no. 1 (November 20, 2020): 100–109. http://dx.doi.org/10.2478/dim-2020-0025.
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AbstractWith increasing globalization, news from different countries, and even in different languages, has become readily available and has become a way for many people to learn about other cultures. As people around the world become more reliant on social media, the impact of fake news on public society also increases. However, most of the fake news detection research focuses only on English. In this work, we compared the difference between textual features of different languages (Chinese and English) and their effect on detecting fake news. We also explored the cross-language transmissibility of fake news detection models. We found that Chinese textual features in fake news are more complex compared with English textual features. Our results also illustrated that the bidirectional encoder representations from transformers (BERT) model outperformed other algorithms for within-language data sets. As for detection in cross-language data sets, our findings demonstrated that fake news monitoring across languages is potentially feasible, while models trained with data from a more inclusive language would perform better in cross-language detection.
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Karnyoto, Andrea, Chengjie Sun, Bingquan Liu, and Xiaolong Wang. "Transfer learning and GRU-CRF augmentation for COVID-19 fake news detection." Computer Science and Information Systems, no. 00 (2021): 53. http://dx.doi.org/10.2298/csis210501053k.
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The spread of fake news on online media is very dangerous and can lead to casualties, effects on psychology, character assassination, elections for political parties, and state chaos. Fake news that concerning Covid-19 massively spread during the pandemic. Detecting misinformation on the Internet is an essential and challenging task since humans face difficulty detecting fake news. We applied BERT and GPT2 as pre-trained using the BiGRU-Att-CapsuleNet model and BiGRU-CRF features augmentation to solve Fake News detection in Constraint @ AAAI2021 - COVID19 Fake News Detection in English Dataset. This research proved that our hybrid model with augmentation got better accuracy compared to our baseline model. It also showed that BERT gave a better result than GPT2 in all models; the highest accuracy we achieved for BERT is 0.9196, and GPT2 is 0.8986.
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Segura-Bedmar, Isabel, and Santiago Alonso-Bartolome. "Multimodal Fake News Detection." Information 13, no. 6 (June 2, 2022): 284. http://dx.doi.org/10.3390/info13060284.
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Over the last few years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have on different segments of our society. Thus, the development of tools for the automatic detection of fake news plays an important role in the prevention of its negative effects. Most attempts to detect and classify false content focus only on using textual information. Multimodal approaches are less frequent and they typically classify news either as true or fake. In this work, we perform a fine-grained classification of fake news on the Fakeddit dataset, using both unimodal and multimodal approaches. Our experiments show that the multimodal approach based on a Convolutional Neural Network (CNN) architecture combining text and image data achieves the best results, with an accuracy of 87%. Some fake news categories, such as Manipulated content, Satire, or False connection, strongly benefit from the use of images. Using images also improves the results of the other categories but with less impact. Regarding the unimodal approaches using only text, Bidirectional Encoder Representations from Transformers (BERT) is the best model, with an accuracy of 78%. Exploiting both text and image data significantly improves the performance of fake news detection.
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Nagalakshmi, E. V., E. Sai Vineeth, Y. Goutham, and T. Vamshi Krishna. "Fake News Detection using Machine Learning - A Working Model of Fake News Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 1540–48. http://dx.doi.org/10.22214/ijraset.2023.51637.
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Abstract: This project aims to address the pressing issue of fake news, which has become increasingly prevalent in today's society. With the internet and social media making news more accessible than ever, the spread of fake news can have a significant impact on social, economic, and political environments. In response to this challenge, this project investigates the use of machine learning algorithms to accurately classify news as real or fake. The project utilizes KNN, Decision Tree, and Logistic Regression algorithms to analyze large datasets of news articles and learn the patterns and characteristics of real and fake news. The primary objective of this project is to provide users with a tool that can accurately detect fake news and help prevent its spread
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Oyeniyi, Samuel A., and Joseph A. Ojeniyi. "DEVELOPMENT OF A CONCEPTUAL FRAMEWORK AND A MEASUREMENT MODEL FOR THE DETECTION OF FAKE NEWS." International Journal of Innovative Research in Advanced Engineering 8, no. 7 (July 30, 2021): 138–47. http://dx.doi.org/10.26562/ijirae.2021.v0807.001.
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Fake news has been there since before the advent of the Internet. It has had an immense impact on our modern society. Detecting fake news is an important step. Although there are various ways and methods in which fake news can be detected and solved. In this research paper we discuss the various conceptual frameworks and how they affect fake news. It further shows the development of the conceptual framework and the measurement model used; showing which of the frameworks fake news is most likely to surface through. The objective of the research is to design a conceptual framework for fake news detection, whereby developing measurement model for fake news detection, and the framework and model are evaluated for fake news detection. Fake news detection approaches can be divided as: creator and user features, news content features and social context features. A survey was taken based on this feature via questionnaire to determine in which feature, fake news can be quickly spotted. Results: Results shows that fake news can be easily spotted in the creator and user feature, this feature was then used to perform a feature selection on a fake news dataset which gave better accuracy.
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Sharma, Udit. "Fake News Detection Using ML." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3668–74. http://dx.doi.org/10.22214/ijraset.2021.37209.
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Fake news is depicted as a story that is made up with an aim to mislead or to swindle the peruser. We have introduced a reaction for the undertaking of phony news disclosure by utilizing Deep Learning structures. Because of various number of instances of phony news the outcome has been an augmentation in the in the spread of phony news. Due to the wide impacts of the immense onsets of phony news, people are conflicting if not by huge helpless finders of phony news. The most liked of such exercises consolidate "boycotts" of sources and producers that are not trustworthy. While these instruments are used to make an inexorably unique complete beginning to end plan, we need to address continuously inconvenient situations where logically strong sources and makers discharge fake news. As, the objective of this endeavor was to make a mechanical assembly for perceiving the language designs that portray phony and confirmed news using AI, AI and customary language getting ready techniques. The consequences of this undertaking exhibit the breaking point with respect to AI and AI to be huge. We have developed a model that gets numerous no of normal indications of veritable and phony news and additionally an application that aides in the portrayal of the order decision.
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Naik, Samrudhi. "Fake News Detection Using NLP." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 2022–31. http://dx.doi.org/10.22214/ijraset.2021.39582.
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Abstract: The spreading of fake news has given rise to many problems in society. It is due to its ability to cause a lot of social and national damage with destructive impacts. Sometimes it gets very difficult to know if the news is genuine or fake. Therefore it is very important to detect if the news is fake or not. "Fake News" is a term used to represent fabricated news or propaganda comprising misinformation communicated through traditional media channels like print, and television as well as nontraditional media channels like social media. Techniques of NLP and Machine learning can be used to create models which can help to detect fake news. In this paper we have presented six LSTM models using the techniques of NLP and ML. The datasets in comma-separated values format, pertaining to political domain were used in the project. The different attributes like the title and text of the news headline/article were used to perform the fake news detection. The results showed that the proposed solution performs well in terms of providing an output with good accuracy, precision and recall. The performance analysis made between all the models showed that the models which have used GloVe and Word2vec method work better than the models using TF-IDF. Further, a larger dataset for better output and also other factors such as the author ,publisher of the news can be used to determine the credibility of the news. Also, further research can also be done on images, videos, images containing text which can help in improving the models in future. Keywords: Fake news detection, LSTM(long short term memory),Word2Vec,TF-IDF,Natural Language Processing.
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Zhou, Xinyi, Atishay Jain, Vir V. Phoha, and Reza Zafarani. "Fake News Early Detection." Digital Threats: Research and Practice 1, no. 2 (July 9, 2020): 1–25. http://dx.doi.org/10.1145/3377478.
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Shu, Kai, Deepak Mahudeswaran, Suhang Wang, and Huan Liu. "Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation." Proceedings of the International AAAI Conference on Web and Social Media 14 (May 26, 2020): 626–37. http://dx.doi.org/10.1609/icwsm.v14i1.7329.
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Consuming news from social media is becoming increasingly popular. However, social media also enables the wide dissemination of fake news. Because of the detrimental effects of fake news, fake news detection has attracted increasing attention. However, the performance of detecting fake news only from news content is generally limited as fake news pieces are written to mimic true news. In the real world, news pieces spread through propagation networks on social media. The news propagation networks usually involve multi-levels. In this paper, we study the challenging problem of investigating and exploiting news hierarchical propagation network on social media for fake news detection.In an attempt to understand the correlations between news propagation networks and fake news, first, we build hierarchical propagation networks for fake news and true news pieces; second, we perform a comparative analysis of the propagation network features from structural, temporal, and linguistic perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection. We further validate the effectiveness of these features from feature importance analysis. We conduct extensive experiments on real-world datasets and demonstrate the proposed features can significantly outperform state-of-the-art fake news detection methods by at least 1.7% with an average F1>0.84. Altogether, this work presents a data-driven view of hierarchical propagation network and fake news and paves the way towards a healthier online news ecosystem.
Dissertations / Theses on the topic "Fake News detection"
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Nordberg, Pontus. "Automatic fake news detection." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18512.
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Due to the large increase in the proliferation of "fake news" in recent years, it has become a widely discussed menace in the online world. In conjunction with this popularity, research of ways to limit the spread has also increased. This paper aims to look at the current research of this area in order to see what automatic fake news detection methods exist and are being developed, which can help online users in protecting themselves against fake news. A systematic literature review is conducted in order to answer this question, with different detection methods discussed in the literature being divided into categories. The consensus which appears from the collective research between categories is also used to identify common elements between categories which are important to fake news detection; notably the relation of headlines and article content, the importance of high-quality datasets, the use of emotional words, and the circulation of fake news in social media groups.
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O'Brien, Nicole (Nicole J. ). "Machine learning for detection of fake news." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119727.
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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 55-56).
Recent political events have lead to an increase in the popularity and spread of fake news. As demonstrated by the widespread effects of the large onset of fake news, humans are inconsistent if not outright poor detectors of fake news. With this, efforts have been made to automate the process of fake news detection. The most popular of such attempts include "blacklists" of sources and authors that are unreliable. While these tools are useful, in order to create a more complete end to end solution, we need to account for more difficult cases where reliable sources and authors release fake news. As such, the goal of this project was to create a tool for detecting the language patterns that characterize fake and real news through the use of machine learning and natural language processing techniques. The results of this project demonstrate the ability for machine learning to be useful in this task. We have built a model that catches many intuitive indications of real and fake news as well as an application that aids in the visualization of the classification decision.
by Nicole O'Brien.
M. Eng.
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Asresu, Yohannes. "Defining fake news for algorithmic deception detection purposes." Thesis, Uppsala universitet, Institutionen för informatik och media, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-390393.
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RAJ, CHAHAT. "CONVOLUTIONAL NEURAL NETWORKERS FOR MULTIMODALS FAKE NEWS DETECTION." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18816.
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An upsurge of false information revolves around the internet. Social media and websites are
flooded with unverified news posts. These posts are comprised of text, images, audio, and
videos. There is a requirement for a system that detects fake content in multiple data
modalities. We have seen a considerable amount of research on classification techniques for
textual fake news detection, while frameworks dedicated to visual fake news detection are
very few. We explored the state-of-the-art methods using deep networks such as CNNs and
RNNs for multi-modal online information credibility analysis. They show rapid improvement
in classification tasks without requiring pre-processing. To aid the ongoing research over fake
news detection using CNN models, we build textual and visual modules to analyze their
performances over multi-modal datasets. We exploit latent features present inside text and
images using layers of convolutions. We see how well these convolutional neural networks
perform classification when provided with only latent features and analyze what type of
images are needed to be fed to perform efficient fake news detection. We propose a multi-
modal Coupled ConvNet architecture that fuses both the data modules and efficiently
classifies online news depending on its textual and visual content. We thence offer a
comparative analysis of the results of all the models utilized over three datasets. The proposed
architecture outperforms various state-of-the-art methods for fake news detection with
considerably high accuracies.
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Kurasinski, Lukas. "Machine Learning explainability in text classification for Fake News detection." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20058.
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Fake news detection gained an interest in recent years. This made researchers try to findmodels that can classify text in the direction of fake news detection. While new modelsare developed, researchers mostly focus on the accuracy of a model. There is little researchdone in the subject of explainability of Neural Network (NN) models constructed for textclassification and fake news detection. When trying to add a level of explainability to aNeural Network model, allot of different aspects have to be taken under consideration.Text length, pre-processing, and complexity play an important role in achieving successfully classification. Model’s architecture has to be taken under consideration as well. Allthese aspects are analyzed in this thesis. In this work, an analysis of attention weightsis performed to give an insight into NN reasoning about texts. Visualizations are usedto show how 2 models, Bidirectional Long-Short term memory Convolution Neural Network (BIDir-LSTM-CNN), and Bidirectional Encoder Representations from Transformers(BERT), distribute their attentions while training and classifying texts. In addition, statistical data is gathered to deepen the analysis. After the analysis, it is concluded thatexplainability can positively influence the decisions made while constructing a NN modelfor text classification and fake news detection. Although explainability is useful, it is nota definitive answer to the problem. Architects should test, and experiment with differentsolutions, to be successful in effective model construction.
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Ghanem, Bilal Hisham Hasan. "On the Detection of False Information: From Rumors to Fake News." Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/158570.
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[ES] En tiempos recientes, el desarrollo de las redes sociales y de las agencias de noticias han traído nuevos retos y amenazas a la web. Estas amenazas han llamado la atención de la comunidad investigadora en Procesamiento del Lenguaje Natural (PLN) ya que están contaminando las plataformas de redes sociales. Un ejemplo de amenaza serían las noticias falsas, en las que los usuarios difunden y comparten información falsa, inexacta o engañosa. La información falsa no se limita a la información verificable, sino que también incluye información que se utiliza con fines nocivos. Además, uno de los desafíos a los que se enfrentan los investigadores es la gran cantidad de usuarios en las plataformas de redes sociales, donde detectar a los difusores de información falsa no es tarea fácil.
Los trabajos previos que se han propuesto para limitar o estudiar el tema de la detección de información falsa se han centrado en comprender el lenguaje de la información falsa desde una perspectiva lingüística. En el caso de información verificable, estos enfoques se han propuesto en un entorno monolingüe. Además, apenas se ha investigado la detección de las fuentes o los difusores de información falsa en las redes sociales.
En esta tesis estudiamos la información falsa desde varias perspectivas. En primer lugar, dado que los trabajos anteriores se centraron en el estudio de la información falsa en un entorno monolingüe, en esta tesis estudiamos la información falsa en un entorno multilingüe. Proponemos diferentes enfoques multilingües y los comparamos con un conjunto de baselines monolingües. Además, proporcionamos estudios sistemáticos para los resultados de la evaluación de nuestros enfoques para una mejor comprensión. En segundo lugar, hemos notado que el papel de la información afectiva no se ha investigado en profundidad. Por lo tanto, la segunda parte de nuestro trabajo de investigación estudia el papel de la información afectiva en la información falsa y muestra cómo los autores de contenido falso la emplean para manipular al lector. Aquí, investigamos varios tipos de información falsa para comprender la correlación entre la información afectiva y cada tipo (Propaganda, Trucos / Engaños, Clickbait y Sátira).
Por último, aunque no menos importante, en un intento de limitar su propagación, también abordamos el problema de los difusores de información falsa en las redes sociales. En esta dirección de la investigación, nos enfocamos en explotar varias características basadas en texto extraídas de los mensajes de perfiles en línea de tales difusores. Estudiamos diferentes conjuntos de características que pueden tener el potencial de ayudar a discriminar entre difusores de información falsa y verificadores de hechos.[CA] En temps recents, el desenvolupament de les xarxes socials i de les agències de notícies han portat nous reptes i amenaces a la web. Aquestes amenaces han cridat l'atenció de la comunitat investigadora en Processament de Llenguatge Natural (PLN) ja que estan contaminant les plataformes de xarxes socials. Un exemple d'amenaça serien les notícies falses, en què els usuaris difonen i comparteixen informació falsa, inexacta o enganyosa. La informació falsa no es limita a la informació verificable, sinó que també inclou informació que s'utilitza amb fins nocius. A més, un dels desafiaments als quals s'enfronten els investigadors és la gran quantitat d'usuaris en les plataformes de xarxes socials, on detectar els difusors d'informació falsa no és tasca fàcil. Els treballs previs que s'han proposat per limitar o estudiar el tema de la detecció d'informació falsa s'han centrat en comprendre el llenguatge de la informació falsa des d'una perspectiva lingüística. En el cas d'informació verificable, aquests enfocaments s'han proposat en un entorn monolingüe. A més, gairebé no s'ha investigat la detecció de les fonts o els difusors d'informació falsa a les xarxes socials. En aquesta tesi estudiem la informació falsa des de diverses perspectives. En primer lloc, atès que els treballs anteriors es van centrar en l'estudi de la informació falsa en un entorn monolingüe, en aquesta tesi estudiem la informació falsa en un entorn multilingüe. Proposem diferents enfocaments multilingües i els comparem amb un conjunt de baselines monolingües. A més, proporcionem estudis sistemàtics per als resultats de l'avaluació dels nostres enfocaments per a una millor comprensió. En segon lloc, hem notat que el paper de la informació afectiva no s'ha investigat en profunditat. Per tant, la segona part del nostre treball de recerca estudia el paper de la informació afectiva en la informació falsa i mostra com els autors de contingut fals l'empren per manipular el lector. Aquí, investiguem diversos tipus d'informació falsa per comprendre la correlació entre la informació afectiva i cada tipus (Propaganda, Trucs / Enganys, Clickbait i Sàtira). Finalment, però no menys important, en un intent de limitar la seva propagació, també abordem el problema dels difusors d'informació falsa a les xarxes socials. En aquesta direcció de la investigació, ens enfoquem en explotar diverses característiques basades en text extretes dels missatges de perfils en línia de tals difusors. Estudiem diferents conjunts de característiques que poden tenir el potencial d'ajudar a discriminar entre difusors d'informació falsa i verificadors de fets.
[EN] In the recent years, the development of social media and online news agencies has brought several challenges and threats to the Web. These threats have taken the attention of the Natural Language Processing (NLP) research community as they are polluting the online social media platforms. One of the examples of these threats is false information, in which false, inaccurate, or deceptive information is spread and shared by online users. False information is not limited to verifiable information, but it also involves information that is used for harmful purposes. Also, one of the challenges that researchers have to face is the massive number of users in social media platforms, where detecting false information spreaders is not an easy job. Previous work that has been proposed for limiting or studying the issue of detecting false information has focused on understanding the language of false information from a linguistic perspective. In the case of verifiable information, approaches have been proposed in a monolingual setting. Moreover, detecting the sources or the spreaders of false information in social media has not been investigated much. In this thesis we study false information from several aspects. First, since previous work focused on studying false information in a monolingual setting, in this thesis we study false information in a cross-lingual one. We propose different cross-lingual approaches and we compare them to a set of monolingual baselines. Also, we provide systematic studies for the evaluation results of our approaches for better understanding. Second, we noticed that the role of affective information was not investigated in depth. Therefore, the second part of our research work studies the role of the affective information in false information and shows how the authors of false content use it to manipulate the reader. Here, we investigate several types of false information to understand the correlation between affective information and each type (Propaganda, Hoax, Clickbait, Rumor, and Satire). Last but not least, in an attempt to limit its spread, we also address the problem of detecting false information spreaders in social media. In this research direction, we focus on exploiting several text-based features extracted from the online profile messages of those spreaders. We study different feature sets that can have the potential to help to identify false information spreaders from fact checkers.
Ghanem, BHH. (2020). On the Detection of False Information: From Rumors to Fake News [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/158570
TESIS
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Wan, Zhibin, and Huatai Xu. "Performance comparison of different machine learningmodels in detecting fake news." Thesis, Högskolan Dalarna, Institutionen för information och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-37576.
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The phenomenon of fake news has a significant impact on our social life, especially in the political world. Fake news detection is an emerging area of research. The sharing of infor-mation on the Web, primarily through Web-based online media, is increasing. The ability to identify, evaluate, and process this information is of great importance. Deliberately created disinformation is being generated on the Internet, either intentionally or unintentionally. This is affecting a more significant segment of society that is being blinded by technology. This paper illustrates models and methods for detecting fake news from news articles with the help of machine learning and natural language processing. We study and compare three different feature extraction techniques and seven different machine classification techniques. Different feature engineering methods such as TF, TF-IDF, and Word2Vec are used to gener-ate feature vectors in this proposed work. Even different machine learning classification al-gorithms were trained to classify news as false or true. The best algorithm was selected to build a model to classify news as false or true, considering accuracy, F1 score, etc., for com-parison. We perform two different sets of experiments and finally obtain the combination of fake news detection models that perform best in different situations.
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Frimodig, Matilda, and Sivertsson Tom Lanhed. "A Comparative study of Knowledge Graph Embedding Models for use in Fake News Detection." Thesis, Malmö universitet, Institutionen för datavetenskap och medieteknik (DVMT), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43228.
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During the past few years online misinformation, generally referred to as fake news, has been identified as an increasingly dangerous threat. As the spread of misinformation online has increased, fake news detection has become an active line of research. One approach is to use knowledge graphs for the purpose of automated fake news detection. While large scale knowledge graphs are openly available these are rarely up to date, often missing the relevant information needed for the task of fake news detection. Creating new knowledge graphs from online sources is one way to obtain the missing information. However extracting information from unstructured text is far from straightforward. Using Natural Language Processing techniques we developed a pre-processing pipeline for extracting information from text for the purpose of creating knowledge graphs. In order to classify news as fake or not fake with the use of knowledge graphs, these need to be converted into a machine understandable format, called knowledge graph embeddings. These embeddings also allow new information to be inferred or classified based on the already existing information in the knowledge graph. Only one knowledge graph embedding model has previously been used for the purpose of fake news detection while several new models have recently been developed. We compare the performance of three different embedding models, all relying on different fundamental architectures, in the specific context of fake news detection. The models used were the geometric model TransE, the tensor decomposition model ComplEx and the deep learning model ConvKB. The results of this study shows that out of the three models, ConvKB is the best performing. However other aspects than performance need to be considered and as such these results do not necessarily mean that a deep learning approach is the most suitable for real world fake news detection.
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Shell, Joshua L. "Bots and Political Discourse: System Requirements and Proposed Methods of Bot Detection and Political Affiliation via Browser Plugin." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592136507505369.
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Abdallah, Abdallah Sabry. "Investigation of New Techniques for Face detection." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/33191.
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The task of detecting human faces within either a still image or a video frame is one of the most popular object detection problems. For the last twenty years researchers have shown great interest in this problem because it is an essential pre-processing stage for computing systems that process human faces as input data. Example applications include face recognition systems, vision systems for autonomous robots, human computer interaction systems (HCI), surveillance systems, biometric based authentication systems, video transmission and video compression systems, and content based image retrieval systems.
In this thesis, non-traditional methods are investigated for detecting human faces within color images or video frames. The attempted methods are chosen such that the required computing power and memory consumption are adequate for real-time hardware implementation. First, a standard color image database is introduced in order to accomplish fair evaluation and benchmarking of face detection and skin segmentation approaches. Next, a new pre-processing scheme based on skin segmentation is presented to prepare the input image for feature extraction. The presented pre-processing scheme requires relatively low computing power and memory needs. Then, several feature extraction techniques are evaluated. This thesis introduces feature extraction based on Two Dimensional Discrete Cosine Transform (2D-DCT), Two Dimensional Discrete Wavelet Transform (2D-DWT), geometrical moment invariants, and edge detection. It also attempts to construct a hybrid feature vector by the fusion between 2D-DCT coefficients and edge information, as well as the fusion between 2D-DWT coefficients and geometrical moments. A self organizing map (SOM) based classifier is used within all the experiments to distinguish between facial and non-facial samples. Two strategies are tried to make the final decision from the output of a single SOM or multiple SOM. Finally, an FPGA based framework that implements the presented techniques, is presented as well as a partial implementation.
Every presented technique has been evaluated consistently using the same dataset. The experiments show very promising results. The highest detection rate of 89.2% was obtained when using a fusion between DCT coefficients and edge information to construct the feature vector. A second highest rate of 88.7% was achieved by using a fusion between DWT coefficients and geometrical moments. Finally, a third highest rate of 85.2% was obtained by calculating the moments of edges.Master of Science
Books on the topic "Fake News detection"
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Shu, Kai, and Huan Liu. Detecting Fake News on Social Media. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01915-9.
Full textBook chapters on the topic "Fake News detection"
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Palacio Marín, Ignacio, and David Arroyo. "Fake News Detection." In 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020), 229–38. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57805-3_22.
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Abhishek, Satyam Kumar, and Manoj Kumar. "Fake News Detection." In Data Intelligence and Cognitive Informatics, 193–207. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6460-1_14.
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Long, Si Hong, and Mohd Pouzi Bin Hamzah. "Fake News Detection." In Lecture Notes in Electrical Engineering, 295–303. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4069-5_25.
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Raj, Ansuman Ravi, Lakshay Kaushik, Aamir Suhail, and B. Santhosh. "Fake News Detection." In Algorithms for Intelligent Systems, 829–37. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3485-0_65.
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Chakraborty, Tanmoy. "Multi-modal Fake News Detection." In Data Science for Fake News, 41–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62696-9_3.
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Sitaula, Niraj, Chilukuri K. Mohan, Jennifer Grygiel, Xinyi Zhou, and Reza Zafarani. "Credibility-Based Fake News Detection." In Lecture Notes in Social Networks, 163–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42699-6_9.
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Verma, Vijay, Mohit Rohilla, Anuj Sharma, and Mohit Gupta. "Fake News Detection on Twitter." In Advances in Data and Information Sciences, 141–49. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5292-0_13.
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Ma, Fanghe, and Guoxian Tan. "NLP in Fake News Detection." In IRC-SET 2020, 71–83. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9472-4_6.
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Trivedi, Sainyali, Mayank Kumar Jain, Dinesh Gopalani, Yogesh Kumar Meena, and Yogendra Gupta. "Fake News Detection: A Study." In Algorithms for Intelligent Systems, 395–408. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1373-2_31.
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G, Santhosh Kumar. "Deep Learning for Fake News Detection." In Data Science for Fake News, 71–100. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62696-9_4.
Full textConference papers on the topic "Fake News detection"
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Jain, Akshay, and Amey Kasbe. "Fake News Detection." In 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2018. http://dx.doi.org/10.1109/sceecs.2018.8546944.
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Nayagam, Selvanathan, Aakash Sankar, Abishek Shanmugasundaram, Akash Palanivel, and Liju Tonny Arokia Robert Raja. "Fake news detection." In 24TH TOPICAL CONFERENCE ON RADIO-FREQUENCY POWER IN PLASMAS. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0164646.
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Islam, Farzana, Mohammad Minhazul Alam, S. M. Shahadat Hossain, Abdul Motaleb, Sabrina Yeasmin, Mehedi Hasan, and Rashedur M. Rahman. "Bengali Fake News Detection." In 2020 IEEE 10th International Conference on Intelligent Systems (IS). IEEE, 2020. http://dx.doi.org/10.1109/is48319.2020.9199931.
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Gangireddy, Siva Charan Reddy, Deepak P, Cheng Long, and Tanmoy Chakraborty. "Unsupervised Fake News Detection." In HT '20: 31st ACM Conference on Hypertext and Social Media. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3372923.3404783.
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Sigmund, Tomáš. "STUDENTS' EVALUATION OF FAKE NEWS DETECTION AND COUNTERMEASURES AGAINST FAKE NEWS." In 14th International Conference on Education and New Learning Technologies. IATED, 2022. http://dx.doi.org/10.21125/edulearn.2022.0648.
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Ameli, Leila, Md Shah Alam Chowdhury, Farnaz Farid, Abubakar Bello, Fariza Sabrina, and Alana Maurushat. "AI and Fake News: A Conceptual Framework for Fake News Detection." In CSW 2022: 2022 International Conference on Cyber Security. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3584714.3584722.
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Shaik, Mohammed Ali, Makkaji Yasha Sree, Sanka Sri Vyshnavi, Thogiti Ganesh, Dasari Sushmitha, and Narmetta Shreya. "Fake News Detection using NLP." In 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA). IEEE, 2023. http://dx.doi.org/10.1109/icidca56705.2023.10100305.
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Majeed, Tabasum, Huma Farooq, Tabassum Jan, and Aabidah Nazir. "Fake News Detection: A Review." In Proceedings of the 3rd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2022, 24-25 March 2022, New Delhi, India. EAI, 2023. http://dx.doi.org/10.4108/eai.24-3-2022.2318561.
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Nguyen, Duc Minh, Tien Huu Do, Robert Calderbank, and Nikos Deligiannis. "Fake News Detection using Deep." In Proceedings of the 2019 Conference of the North. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-1141.
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Deshmukh, Ashwini Ashutosh, and Sharvari Govilkar. "Fake News Detection on Datasets." In 2022 5th International Conference on Advances in Science and Technology (ICAST). IEEE, 2022. http://dx.doi.org/10.1109/icast55766.2022.10039650.
Full textReports on the topic "Fake News detection"
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Graham, Timothy, and Katherine M. FitzGerald. Bots, Fake News and Election Conspiracies: Disinformation During the Republican Primary Debate and the Trump Interview. Queensland University of Technology, 2023. http://dx.doi.org/10.5204/rep.eprints.242533.
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We used Alexandria Digital, a world leading disinformation detection technology, to analyse almost a million posts on X (formerly known as Twitter) and Reddit comments during the first Republican primary debate and counterprogrammed Tucker Carlson and Donald Trump interview on the 23rd of August. What we did: • Collected 949,259 posts from the platform X, formerly known as Twitter. These posts were collected if they contained one of 11 relevant hashtags or keywords and were posted between 8:45pm and 11:15pm EST on 23rd August 2023. • Collected 20,549 comments from two separate Reddit threads. Both were discussion threads dedicated to the first Republican primary Debate and the Tucker Carlson and Donald Trump interview from r/Conservative and r/politics. • This methodology allowed us to capture narratives and conduct analysis of coordinated behaviour that occurred immediately before, during, and after the Republican primary debate and the airing of the Tucker Carlson interview of Donald Trump. What we found: • A coordinated network of over 1200 accounts promoting the conspiracy theory that Donald Trump won the 2020 United States presidential election that received over 3 million impressions on the platform X; • A sprawling bot network consisting of 1,305 unique accounts with a variety of clusters; • Some of the largest clusters were coordinated troll networks in support of Donald Trump; a coordinated network of misleading news outlets, and a clickbait Pro-Trump bot network. • No coordinated activity was found on Reddit during the Republican Primary Debate or in discussion of the Tucker Carlson and Donald Trump interview. What does this mean? • X is flooded with platform manipulation of various kinds, is not doing enough to moderate content, and has no clear strategy for dealing with political disinformation. • A haven for disinformation. While pre-Musk Twitter previously managed to moderate harmful conspiracy theories such as QAnon, X is now a safe space for conspiracy theorists and political disinformation. • That no evidence of coordinated influence activity was found on Reddit suggests the extensive rules and moderation either prevented or removed coordinated activity from the platform. • Worrying trends. Given the prevalence of mis- and disinformation during the debate and interview, the leadup to the US 2024 Presidential Election is likely to witness a surge of information disorder on the platform. • Trump is back. The reinstatement of Donald Trump’s X account has emboldened conspiracy theorists and the far right, who are interpreting this as a sign that the reason why Trump was suspended (incitement to violence) validates election fraud disinformation and activism. • Anything goes. The lack of a freely available Twitter Application Programming Interface (API) means that researchers, journalists, and regulators cannot monitor disinformation on X and hold the platform to account.
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Hedrick, Ronald, and Herve Bercovier. Characterization and Control of KHV, A New Herpes Viral Pathogen of Koi and Common Carp. United States Department of Agriculture, January 2004. http://dx.doi.org/10.32747/2004.7695871.bard.
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In this project we proposed to characterize the virus genome and the structural virion polypeptides to allow development of improved diagnostic approaches and potential vaccination strategies. These goals have been mostly achieved and the corresponding data were published in three papers (see below) and three more manuscripts are in preparation. The virion polypeptides of KHV strains isolated from USA (KHV-U) and Israel (KHV-I) were found to be identical. Purified viral DNA analyzed with a total of 5 restriction enzymes demonstrated no fragment length polymorphism between KHV-I and KHV-U but both KHV isolates differed significantly from the cyprinid herpesvirus (CHV) and the ictalurid herpesvirus (channel catfish virus or CCV). Using newly obtained viral DNA sequences two different PCR assays were developed that need to be now further tested in the field. We determined by pulse field analysis that the size of KHV genome is around 280 kbp (1-1. Bercovier, unpublished results). Sequencing of the viral genome of KHV has reached the stage where 180 kbp are sequenced (twice and both strands). Four hypothetical genes were detected when DNA sequences were translated into amino acid sequences. The finding of a gene of real importance, the thymidine kinase (TK) led us to extend the study of this specific gene. Four other genes related to DNA synthesis were found. PCR assays based on defined sequences were developed. The PCR assay based on TK gene sequence has shown improved sensitivity in the detection of KHV DNA compared to regular PCR assays. </P> <P><SPAN>With the ability to induce experimental infections in koi with KHV under controlled laboratory conditions we have studied the progress and distribution of virus in host tissues, the development of immunity and the establishment of latent infections. Also, we have investigated the important role of water temperature on severity of infections and mortality of koi following infections with KHV. These initial studies need to be followed by an increased focus on long-term fate of the virus in survivors. This is essential in light of the current "controlled exposure program" used by farmers to produce KHV "naturally resistant fish" that may result in virus or DNA carriers. </SPAN></P> <P><SPAN>The information gained from the research of this project was designed to allow implementation of control measures to prevent the spread of the virus both by improved diagnostic approaches and preventive measures. We have accomplished most of these goals but further studies are needed to establish even more reliable methods of prevention with increased emphases on improved diagnosis and a better understanding of the ecology of KHV. </SPAN>
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Doo, Johnny. Unsettled Issues Concerning eVTOL for Rapid-response, On-demand Firefighting. SAE International, August 2021. http://dx.doi.org/10.4271/epr2021017.
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Recent advancements of electric vertical take-off and landing (eVTOL) aircraft have generated significant interest within and beyond the traditional aviation industry, and many novel applications have been identified and are in development. One promising application for these innovative systems is in firefighting, with eVTOL aircraft complementing current firefighting capabilities to help save lives and reduce fire-induced damages. With increased global occurrences and scales of wildfires—not to mention the issues firefighters face during urban and rural firefighting operations daily—eVTOL technology could offer timely, on-demand, and potentially cost-effective aerial mobility capabilities to counter these challenges. Early detection and suppression of wildfires could prevent many fires from becoming large-scale disasters. eVTOL aircraft may not have the capacity of larger aerial assets for firefighting, but targeted suppression, potentially in swarm operations, could be valuable. Most importantly, on-demand aerial extraction of firefighters can be a crucial benefit during wildfire control operations. Aerial firefighter dispatch from local fire stations or vertiports can result in more effective operations, and targeted aerial fire suppression and civilian extraction from high-rise buildings could enhance capabilities significantly. There are some challenges that need to be addressed before the identified capabilities and benefits are realized at scale, including the development of firefighting-specific eVTOL vehicles; sense and avoid capabilities in complex, smoke-inhibited environments; autonomous and remote operating capabilities; charging system compatibility and availability; operator and controller training; dynamic airspace management; and vehicle/fleet logistics and support. Acceptance from both the first-responder community and the general public is also critical for the successful implementation of these new capabilities. The purpose of this report is to identify the benefits and challenges of implementation, as well as some of the potential solutions. Based on the rapid development progress of eVTOL aircraft and infrastructures with proactive community engagement, it is envisioned that these challenges can be addressed soon. NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. These reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.
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Perk, Simon, Egbert Mundt, Alexander Panshin, Irit Davidson, Irina Shkoda, Ameera AlTori, and Maricarmen Garcia. Characterization and Control Strategies of Low Pathogenic Avian Influenza Virus H9N2. United States Department of Agriculture, November 2012. http://dx.doi.org/10.32747/2012.7697117.bard.
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The avian influenza virus, subtype H9N2 subtype, defined as having a low pathogenicity, causes extensive economical losses in commercial flocks, probably due to management and synergism with other pathogens. AIV H9N2 was first identified in Israel in the year 2000, and since then it became endemic and widespread in Israel. Control by vaccination of commercial flocks with an inactivated vaccine has been introduced since 2007. In face of the continuous H9N2 outbreaks, and the application of the vaccination policy, we aimed in the present study to provide a method of differentiating naturally infected from vaccinated animals (DIVA). The aim of the assay would be detect only antibodies created by a de-novo infection, since the inactivated vaccine virus is not reproducing, and might provide a simple tool for mass detection of novel infections of commercial flocks. To fulfill the overall aim, the project was designed to include four operational objectives: 1. Evaluation of the genetic evolution of AIV in Israel; 2. Assessment of the diagnostic value of an NS1 ELISA; 3. NS1 ELISA as evaluation criteria for measuring the efficacy of vaccination against H9N2 AIV; 4. Development of an AIV H9 subtype specific ELISA systems. Major conclusion and implications drawn from the project were: 1. A continuous genetic change occurred in the collection of H9N2 isolates, and new introductions were identified. It was shown thatthe differences between the HA proteins of viruses used for vaccine productionand local fieldisolatesincreasedin parallelwith the durationand intensity ofvaccine use, therefore, developing a differential assay for the vaccine and the wild type viruses was the project main aim. 2. To assess the diagnostic value of an NS1 ELISA we first performed experimental infection trials using representative viruses of all introductions, and used the sera and recombinant NS1 antigens of the same viruses in homologous and heterologous NS1 ELISA combination. The NS1 ELISA was evidently reactive in all combinations, and did not discriminate significantly between different groups. 3. However, several major drawbacks of the NS1 ELISA were recognized: a) The evaluation of the vaccination effect in challenged birds, showed that the level of the NS1 antibodies dropped due to the vaccination-dependent virus level drop; b) the applicability of the NS1-ELISA was verified on sera of commercial flocks and found to be unusable due to physico-chemical composition of the sera and the recombinant antigen, c) commercial sera showed non-reactivity that might be caused by many factors, including vaccination, uncertainty regarding the infection time, and possibly low antigen avidity, d) NS1 elevated antibody levels for less than 2 months in SPF chicks. Due to the above mentioned reasons we do not recommend the application of the DIVA NS1 ELISA assay for monitoring and differentiation AIV H9N2 naturally-infected from vaccinated commercial birds.
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Bryant, C. A., S. A. Wilks, and C. W. Keevil. Survival of SARS-CoV-2 on the surfaces of food and food packaging materials. Food Standards Agency, November 2022. http://dx.doi.org/10.46756/sci.fsa.kww583.
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COVID-19, caused by the SARS-CoV-2 virus, was first reported in China in December 2019. The virus has spread rapidly around the world and is currently responsible for 500 million reported cases and over 6.4 million deaths. A risk assessment published by the Foods Standards Agency (FSA) in 2020 (Opens in a new window) concluded that it was very unlikely that you could catch coronavirus via food. This assessment included the worst-case assumption that, if food became contaminated during production, no significant inactivation of virus would occur before consumption. However, the rate of inactivation of virus on products sold at various temperatures was identified as a key uncertainty, because if inactivation does occur more rapidly in some situations, then a lower risk may be more appropriate. This project was commissioned to measure the rate of inactivation of virus on the surface of various types of food and food packaging, reducing that uncertainty. The results will be used to consider whether the assumption currently made in the risk assessment remains appropriate for food kept at a range of temperatures, or whether a lower risk is more appropriate for some. We conducted a laboratory-based study, artificially contaminating infectious SARS-CoV-2 virus onto the surfaces of foods and food packaging. We measured how the amount of infectious virus present on those surfaces declined over time, at a range of temperatures and relative humidity levels, reflecting typical storage conditions. We tested broccoli, peppers, apple, raspberry, cheddar cheese, sliced ham, olives, brine from the olives, white and brown bread crusts, croissants and pain au chocolat. The foods tested were selected as they are commonly sold loose on supermarket shelves or uncovered at deli counters or market stalls, they may be difficult to wash, and they are often consumed without any further processing i.e. cooking. The food packaging materials tested were polyethylene terephthalate (PET1) trays and bottles; aluminium cans and composite drinks cartons. These were selected as they are the most commonly used food packaging materials or consumption of the product may involve direct mouth contact with the packaging. Results showed that virus survival varied depending on the foods and food packaging examined. In several cases, infectious virus was detected for several hours and in some cases for several days, under some conditions tested. For a highly infectious agent such as SARS-CoV-2, which is thought to be transmissible by touching contaminated surfaces and then the face, this confirmation is significant. For most foods tested there was a significant drop in levels of virus contamination over the first 24 hours. However, for cheddar cheese and sliced ham, stored in refrigerated conditions and a range of relative humidity, the virus levels remained high up to a week later, when the testing period was stopped. Both cheddar cheese and sliced ham have high moisture, protein and saturated fat content, possibly offering protection to the virus. When apples and olives were tested, the virus was inactivated to the limit of detection very quickly, within an hour, when the first time point was measured. We suggest that chemicals, such as flavonoids, present in the skin of apples and olives inactivate the virus. The rate of viral decrease was rapid, within a few hours, for croissants and pain au chocolat. These pastries are both coated with a liquid egg wash, which may have an inhibitory effect on the virus. Food packaging materials tested had variable virus survival. For all food packaging, there was a significant drop in levels of virus contamination over the first 24 hours, in all relative humidity conditions and at both 6°C and 21°C; these included PET1 bottles and trays, aluminium cans and composite drinks cartons.
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European experts develop a new framework to screen early ASD. ACAMH, May 2018. http://dx.doi.org/10.13056/acamh.10551.
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Early detection of Autism Spectrum Disorder (ASD) can improve outcomes for children, yet the effectiveness and validity of universal screening methods has been questioned. Now, researchers have created a new framework to generate a valid early ASD screening method using a novel approach based on “face and content validity”.