Fake News Detection in Social Media Using Machine Learning and Deep Learning

Fake news detection in social media is a process of detecting false information that is intentionally created to mislead readers. The spread of fake news may cause social, economic, and political turmoil if their proliferation is not prevented. However, fake news detection using machine learning faces many challenges. Datasets of fake news are usually unstructured and noisy. Fake news often mimics true news. In this study, a data preprocessing method is proposed for mitigating missing values in the datasets to enhance fake news detection accuracy. The experimental results show that Multi- Layer Perceptron (MLP) classifier combined with the proposed data preprocessing method outperforms the state-of-the-art methods. Furthermore, to improve the early detection of rumors in social media, a time-series model is proposed for fake news detection in social media using Twitter data. With the proposed model, computational complexity has been reduced significantly in terms of machine learning models training and testing times while achieving similar results as state-of-the-art in the literature. Besides, the proposed method has a simplified feature extraction process, because only the temporal features of the Twitter data are used. Moreover, deep learning techniques are also applied to fake news detection. Experimental results demonstrate that deep learning methods outperformed traditional machine learning models. Specifically, the ensemble-based deep learning classification model achieved top performance

Microbloggers’ motivations in participatory journalism: A cross-cultural study of America and China

This phenomenological study focuses on the motivations of participatory journalists contributing on microblogs such as Twitter and Weibo. Although online user behavior and motivations have been studied before, few studies have examined motivations of participatory journalists from their own perspective. Moreover, this study is one of the few to explore participatory journalists across different cultures (U.S. and China). The author conducted a total of 13 in-depth interviews with participatory journalists on microblogs from both countries and used a qualitative analysis method to identify the themes and patterns that emerged. Motivations such as earning respect, technology early adoption, self-expression, relationship building, self-enhancement, branding and image building, and financial gain were discussed. De-motivational factors such as time constraints and self-censorship were presented. Motivational differences between the two groups of participants, including what the microblog account represents and the role of participatory journalists, were explained by cultural differences collectivism versus individualism and power distance. Limitations and future research were also discussed

Mapping (Dis-)Information Flow about the MH17 Plane Crash

Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets, or used proxys for data annotation. In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though we find that a neural classifier improves over a hashtag based baseline, labeling pro-Russian and pro-Ukrainian content with high precision remains a challenging problem. We provide an error analysis underlining the difficulty of the task and identify factors that might help improve classification in future work. Finally, we show how the classifier can facilitate the annotation task for human annotators

On the Detection of False Information: From Rumors to Fake News

Tesis por compendio[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/10251/158570TESISCompendi

Social informatics

5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings</p

Event identification in social media using classification-clustering framework

In recent years, there has been increased interest in real-world event detection using publicly accessible data made available through Internet technology such as Twitter, Facebook and YouTube. In these highly interactive systems the general public are able to post real-time reactions to “real world" events - thereby acting as social sensors of terrestrial activity. Automatically detecting and categorizing events, particularly smallscale incidents, using streamed data is a non-trivial task, due to the heterogeneity, the scalability and the varied quality of the data as well as the presence of noise and irrelevant information. However, it would be of high value to public safety organisations such as local police, who need to respond accordingly. To address these challenges we present an end-to-end integrated event detection framework which comprises five main components: data collection, pre-processing, classification, online clustering and summarization. The integration between classification and clustering enables events to be detected, especially “disruptive events" - incidents that threaten social safety and security, or that could disrupt social order. We present an evaluation of the effectiveness of detecting events using a variety of features derived from Twitter posts, namely: temporal, spatial and textual content. We evaluate our framework on large-scale, realworld datasets from Twitter and Flickr. Furthermore, we apply our event detection system to a large corpus of tweets posted during the August 2011 riots in England. We show that our system can perform as well as terrestrial sources, such as police reports, traditional surveillance, and emergency calls, even better than local police intelligence in most cases. The framework developed in this thesis provides a scalable, online solution, to handle the high volume of social media documents in different languages including English, Arabic, Eastern languages such as Chinese, and many Latin languages. Moreover, event detection is a concept that is crucial to the assurance of public safety surrounding real-world events. Decision makers use information from a range of terrestrial and online sources to help inform decisions that enable them to develop policies and react appropriately to events as they unfold. Due to the heterogeneity and scale of the data and the fact that some messages are more salient than others for the purposes of understanding any risk to human safety and managing any disruption caused by events, automatic summarization of event-related microblogs is a non-trivial and important problem. In this thesis we tackle the task of automatic summarization of Twitter posts, and present three methods that produce summaries by selecting the most representative posts from real-world tweet-event clusters. To evaluate our approaches, we compare them to the state-of-the-art summarization systems and human generated summaries. Our results show that our proposed methods outperform all the other summarization systems for English and non-English corpora

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains