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

FacTweet: Profiling Fake News Twitter Accounts

[EN] We present an approach to detect fake news in Twitter at the account level using a neural recurrent model and a variety of different semantic and stylistic features. Our method extracts a set of features from the timelines of news Twitter accounts by reading their posts as chunks, rather than dealing with each tweet independently. We show the experimental benefits of modeling latent stylistic signatures of mixed fake and real news with a sequential model over a wide range of strong baselinesThe work of Paolo Rosso was partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31)Ghanem, BHH.; Ponzetto, SP.; Rosso, P. (2020). FacTweet: Profiling Fake News Twitter Accounts. 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Fake accounts detection system based on bidirectional gated recurrent unit neural network

Online social networks have become the most widely used medium to interact with friends and family, share news and important events or publish daily activities. However, this growing popularity has made social networks a target for suspicious exploitation such as the spreading of misleading or malicious information, making them less reliable and less trustworthy. In this paper, a fake account detection system based on the bidirectional gated recurrent unit (BiGRU) model is proposed. The focus has been on the content of users’ tweets to classify twitter user profile as legitimate or fake. Tweets are gathered in a single file and are transformed into a vector space using the GloVe word embedding technique in order to preserve the semantic and syntax context. Compared with the baseline models such as long short-term memory (LSTM) and convolutional neural networks (CNN), the results are promising and confirm that using GloVe with BiGRU classifier outperforms with 99.44% for accuracy and 99.25% for precision. To prove the efficiency of our approach the results obtained with GloVe were compared to Word2vec under the same conditions. Results confirm that GloVe with BiGRU classifier performs the best results for detection of fake Twitter accounts using only tweets content feature

Identifying personality and topics of social media

Title from PDF of title page viewed January 27, 2020Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (pages 37-39)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2019Twitter and Facebook are the renowned social networking platforms where users post, share, interact and express to the world, their interests, personality, and behavioral information. User-created content on social media can be a source of truth, which is suitable to be consumed for the personality identification of social media users. Personality assessment using the Big 5 personality factor model benefits organizations in identifying potential professionals, future leaders, best-fit candidates for the role, and build effective teams. Also, the Big 5 personality factors help to understand depression symptoms among aged people in primary care. We had hypothesized that understanding the user personality of the social network would have significant benefits for topic modeling of different areas like news, towards understanding community interests, and topics. In this thesis, we will present a multi-label personality classification of the social media data and topic feature classification model based on the Big 5 model. We have built the Big 5 personality classification model using a Twitter dataset that has defined openness, conscientiousness, extraversion, agreeableness, and neuroticism. In this thesis, we (1) conduct personality detection using the Big 5 model, (2) extract the topics from Facebook and Twitter data based on each personality, (3) analyze the top essential topics, and (4) find the relation between topics and personalities. The personality would be useful to identify what kind of personality, which topics usually talk about in social media. Multi-label classification is done using Multinomial Naïve Bayes, Logistic Regression, Linear SVC. Topic Modeling is done based on LDA and KATE. Experimental results with Twitter and Facebook data demonstrate that the proposed model has achieved promising results.Introduction -- Background and related work -- Proposed framework -- Results and evaluations -- Conclusion and future wor

Mean birds: Detecting aggression and bullying on Twitter

In recent years, bullying and aggression against social media users have grown significantly, causing serious consequences to victims of all demographics. Nowadays, cyberbullying affects more than half of young social media users worldwide, suffering from prolonged and/or coordinated digital harassment. Also, tools and technologies geared to understand and mitigate it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of bullies and aggressors, and what features distinguish them from regular users. We find that bullies post less, participate in fewer online communities, and are less popular than normal users. Aggressors are relatively popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, with over 90% AUC

TrollBus, An Empirical Study Of Features For Troll Detection

No atual contexto de redes sociais, a discussão política tornou-se um evento normal. Utilizadores de todos os segmentos do espetro político têm a possibilidade de expressar as suas opiniões livremente e discutir as suas visões em várias redes sociais, incluindo o Twitter. Desde 2016, um grupo de utilizadores cujo objetivo é polarizar discussões e semear a discórdia começou a ganhar notoriedade nesta rede social. Estas contas são conhecidas como Trolls, e têm sido ligadas a vários eventos na história recente, tais como a interferência em eleições e a organização de manifestações violentas. Desde a sua descoberta, vários trabalhos de investigação têm sido realizados de modo a detetar estas contas através de machine learning. As abordagens existentes usaram tipos diferentes de atributos. O objetivo deste trabalho é comparar esses grupos de atributos. Para tal, um estudo empírico foi realizado, no qual estes atributos são adaptados à comunidade portuguesa do Twitter. O objetivo deste trabalho foi de analisar as múltiplas abordagens realizadas para a deteção de trolls, com uma descrição das suas features e a sua comparação, quer individualmente quer em grupo. Para tal, um estudo empírico foi realizado, em que estas features são adaptadas à comunidade portuguesa do Twitter. Os dados para este projeto foram recolhidos através do SocialBus, uma ferramenta para a recolha, processamento e armazenamento de dados de redes sociais, nomeadamente do Twitter. O conjunto de contas usado para a recolha de dados foi obtido a partir de jornalistas de política portugueses, e a anotação de trolls foi realizada através de um conjunto restrito de regras comportamentais, auxiliada por uma função de pontuação. Um novo módulo para esta plataforma foi desenvolvido, chamado Trollbus, que realiza a deteção de trolls em tempo real. Um dataset público foi também disponibilizado. Os atributos do melhor modelo combinam os metadados do perfil de uma conta com os aspetos superficiais presentes no seu texto. O grupo de atributos mais importantes revelou ser os aspetos numéricos dos dados, com o mais importante a revelar ser a presença de insultos políticos.In today's social network context, the discussion of politics online has become a normal event. Users from all sides of the political spectrum are able to express their opinions freely and discuss their views in various social networks, including Twitter. From 2016 onward, a group of users whose objective is to polarize discussions and sow discord began to gain notoriety in this social network. These accounts are known as Trolls, and they have been linked to several events in recent history such as the influencing of elections and the organizing of violent protests. Since their discovery, several approaches have been developed to detect these accounts using machine learning techniques. Existing approaches have used different types of features. The goal of this work is to compare those different sets of features. To do so, an empirical study was performed, which adapts these features to the Portuguese Twitter community. The necessary data was collected through SocialBus, a tool for the collection, processing and storage of data from social networks, namely Twitter. The set of accounts used to collect the data were obtained from Portuguese political journalists and the labelling of trolls was performed with a strict set of behavioural rules, aided by a scoring function. A new module for SocialBus was developed, called Trollbus, which performs troll detection in real time. A public dataset was also released. The features of the best model obtained combine an account's profile metadata with the superficial aspects present in its text. The most important feature set noted to be the numerical aspects of the text, with the most important feature revealing to be the presence of political insults