False News On Social Media: A Data-Driven Survey

In the past few years, the research community has dedicated growing interest to the issue of false news circulating on social networks. The widespread attention on detecting and characterizing false news has been motivated by considerable backlashes of this threat against the real world. As a matter of fact, social media platforms exhibit peculiar characteristics, with respect to traditional news outlets, which have been particularly favorable to the proliferation of deceptive information. They also present unique challenges for all kind of potential interventions on the subject. As this issue becomes of global concern, it is also gaining more attention in academia. The aim of this survey is to offer a comprehensive study on the recent advances in terms of detection, characterization and mitigation of false news that propagate on social media, as well as the challenges and the open questions that await future research on the field. We use a data-driven approach, focusing on a classification of the features that are used in each study to characterize false information and on the datasets used for instructing classification methods. At the end of the survey, we highlight emerging approaches that look most promising for addressing false news

Utilizing Multi-modal Weak Signals to Improve User Stance Inference in Social Media

Social media has become an integral component of the daily life. There are millions of various types of content being released into social networks daily. This allows for an interesting view into a users\u27 view on everyday life. Exploring the opinions of users in social media networks has always been an interesting subject for the Natural Language Processing researchers. Knowing the social opinions of a mass will allow anyone to make informed policy or marketing related decisions. This is exactly why it is desirable to find comprehensive social opinions. The nature of social media is complex and therefore obtaining the social opinion becomes a challenging task. Because of how diverse and complex social media networks are, they typically resonate with the actual social connections but in a digital platform. Similar to how users make friends and companions in the real world, the digital platforms enable users to mimic similar social connections. This work mainly looks at how to obtain a comprehensive social opinion out of social media network. Typical social opinion quantifiers will look at text contributions made by users to find the opinions. Currently, it is challenging because the majority of users on social media will be consuming content rather than expressing their opinions out into the world. This makes natural language processing based methods impractical due to not having linguistic features. In our work we look to improve a method named stance inference which can utilize multi-domain features to extract the social opinion. We also introduce a method which can expose users opinions even though they do not have on-topical content. We also note how by introducing weak supervision to an unsupervised task of stance inference we can improve the performance. The weak supervision we bring into the pipeline is through hashtags. We show how hashtags are contextual indicators added by humans which will be much likelier to be related than a topic model. Lastly we introduce disentanglement methods for chronological social media networks which allows one to utilize the methods we introduce above to be applied in these type of platforms

Fake news classification in European Portuguese language

All over the world, many initiatives have been taken to fight fake news. Governments (e.g., France, Germany, United Kingdom and Spain), on their own way, started to take actions regarding legal accountability for those who manufacture or propagate fake news. Different media outlets have also taken plenty initiatives to deal with this phenomenon, such as the increase of the discipline, accuracy and transparency of publications made internally. Some structural changes have been made in those companies and in other entities in order to evaluate news in general. Many teams were built entirely to fight fake news, the so-called “fact-checkers”. Those teams have been adopting different types of techniques in order to do those tasks: from the typical use of journalists, to find out the true behind a controversial statement, to data-scientists, in order to apply forefront techniques such as text mining, and machine learning to support journalist’s decisions. Many of those entities, which aim to maintain or rise their reputation, started to focus on high standards of quality and reliable information, which led to the creation of official and dedicated departments of fact-checking. In the first part of this work, we contextualize European Portuguese language regarding fake news detection and classification, against the current state-of-the-art. Then, we present an end-to-end solution to easily extract and store previously classified European Portuguese news. We used the extracted data to apply some of the most used text minning and machine learning techniques, presented in the current state-of-the-art, in order to understand and evaluate possible limitations of those techniques, in this specific context.Um pouco por todo o mundo foram tomadas várias iniciativas para combater fake news. Muitos governos (França, Alemanha, Reino Unido e Espanha, por exemplo), à sua maneira, começaram a tomar medidas relativamente à responsabilidade legal para aqueles que fabricam ou propagam notícias falsas. Foram feitas algumas mudanças estruturais nos meios de comunicação sociais, a fim de avaliar as notícias em geral. Muitas equipas foram construídas inteiramente para combater fake news, mais especificamente, os denominados "fact-checkers". Essas equipas têm vindo a adotar diferentes tipos de técnicas para realizar as suas tarefas: desde o uso dos jornalistas para descobrir a verdade por detrás de uma declaração controversa, até aos cientistas de dados, que através de técnicas mais avançadas como as técnicas de Text Minning e métodos de classificação de Machine Learning, apoiam as decisões dos jornalistas. Muitas das entidades que visam manter ou aumentar a sua reputação, começaram a concentrar-se em elevados padrões de qualidade e informação fiável, o que levou à criação de departamentos oficiais e dedicados de verificação de factos. Na primeira parte deste trabalho, contextualizamos o Português Europeu no âmbito da detecção e classificação de notícias falsas, fazendo um levantamento do seu actual estado da arte. De seguida, apresentamos uma solução end-to-end que permite facilmente extrair e armazenar notícias portuguesas europeias previamente classificadas. Utilizando os dados extraídos aplicámos algumas das técnicas de Text Minning e de Machine Learning mais utilizadas, apresentadas na literatura, a fim de compreender e avaliar as possíveis limitações dessas técnicas, neste contexto em específic