Toward Discourse-Aware Models for Multilingual Fake News Detection (2021)

Abstract: Statements that are intentionally misstated (or manipulated) are of considerable interest to researchers, government, security, and financial systems. According to deception literature, there are reliable cues for detecting deception and the belief that liars give off cues that may indicate their deception is near-universal. Therefore, given that deceiving actions require advanced cognitive development that honesty simply does not require, as well as people’s cognitive mechanisms have promising guidance for deception detection, in this Ph.D. ongoing research, we propose to examine discourse structure patterns in multilingual deceptive news corpora using the Rhetorical Structure Theory framework. Considering that our work is the first to exploit multilingual discourse-aware strategies for fake news detection, the research community currently lacks multilingual deceptive annotated corpora. Accordingly, this paper describes the current progress in this thesis, including (i) the construction of the first multilingual deceptive corpus, which was annotated by specialists according to the Rhetorical Structure Theory framework, and (ii) the introduction of two new proposed rhetorical relations: INTERJECTION and IMPERATIVE, which we assume to be relevant for the fake news detection task.

## introduction according to @xcite , liars may be identified by their words, and a fairly straightforward element to mitigate risks of deceptive activities consists of the ability to correctly interpret and codify the underlying intent of the speakers @xcite . several models have proposed to automatically identify statements that are intentionally misstated (or manipulated). the vast majority of deception detection methods rely on linguistic features such as n-grams, language complexity, part-of-speech tags, and syntactic and semantic features. more recently, discourse frameworks of the computational linguistics area, such as rhetorical structure theory (rst) @xcite , have been adopted as the basis to automatically detect deceptive stories (e.g., fake news or fake reviews) on the web and social media. figure 1 shows an example of a fake news annotated using the rhetorical structure theory framework. as shown in figure 1 , a deceptive story (also called fake news or deceptive news) was segmented into three elementary discourse units (edu's), as well as hierarchically organized according to the following coherence relations: justify and se-quence. moreover, there are spans of texts that are more central @xcite to the text's purpose than others (satellite), and the nucleus is signaled by an arrow. @xcite argue that research on people's cognitive mechanisms has promising guidance for the detection and refutation of fake content. nevertheless, most approaches for fake news detection are focused on the contentbased classification, as well as "shallow" linguistic features, even without considering the potential of cognitive mechanisms or contextual information. therefore, taking into account the considerable impact of discourse and contextual information for deception detection, we allow ourselves to argue that would be a carelessness rely not on rich and more sophisticated approaches for the fake news detection task. although there is an acknowledged potential for a discourse-aware analysis as a cognitive-based approach, there is a considerable lack of research on discourse in deceptive stories. to @xcite , we do not have significant knowledge of the embedded lies in text or discourse, with the notable exception of the studies proposed in @xcite and @xcite , although these studies deal with fictional discourse. indeed, a very plausible assumption to explain the lack of research on deception in real discourses would be that this is rather a difficult requirement to satisfy. more recent proposals have explored discourseaware models for deception detection, more specifically, fake news and fake reviews detection, nevertheless, to the best of our knowledge, no previous multilingual discourse-aware studies exist. the first discourse approach for fake news detection was proposed by @xcite , in which a discourse-aware model that embodies rhetorical structure features using classical learning methods was used for the english language. in the same settings, for the russian language, @xcite al. (2020), and pisarevskaya (2017) also developed a classical bag-of-rst representation using support vector machine (svm) @xcite and random forest @xcite , as well as pre-trained contextual embeddings (e.g., bert) were used for fake news detection. in @xcite , a robust framework was proposed, which embodies discourse features extracted from a rst-discourse parser, using long short-term memory (lstm) @xcite and convolutional neural networks (cnn) sophisticated learning methods for fake news detection in english. the authors claim that discourse and contextual features improve the fake news detection system performance. for fake reviews detection, @xcite analyzes rhetorical structure coherence relations from a forensic collection of authentic and fake amazon book reviews and concludes that paid review writers deploy deceptive pragmatics (i.e., a coherent set of linguistic strategies) are deployed to support the intent to deceive. in this thesis, we embrace the challenges and opportunities of the study on the rhetorical structure patterns in fake news from different languages. the main goal of this thesis is to assess whether, from the study on the discourse structure patterns in a multilingual fake news corpora, it is possible to propose efficient computational discourse-aware models for multilingual fake news detection. accordingly, this investigation is an inquiry into the nature of the high-level, discourse structure of deceptive natural language texts, whereby the longterm main goal is to uncover systematic language differences and inform them for deception or falsification verification systems. in what follows, we will give a summary of our research effort so far. in section 2, we briefly discuss decetive language, as well the state-of-the-art on discourse structure applied to deception detection. moving forward, in section 3, we present this ongoing research and describe the progress toward this thesis's goals by (1) the construction of the first multilingual deceptive corpus annotated according to the rhetorical structure theory framework, and (2) introducing two new proposed rhetorical relations: interjection and imperative, which we suggest to be relevant for fake news detection. section 4 describes the future research paths for further exploration. in section 5, some conclusions are presented. ## deceptive language and discourse information is mainly passed on in the form of language. consequently, research on how people use language to inform and misinform, as well as how meaning may be automatically extracted from large amounts of text, is unquestionably relevant. moreover, the main literature on deception suggests that liars may be identified by their stories @xcite @xcite . according to @xcite , deceptive linguistic style in general presents negative polarity, weak employment of singular and third-person pronouns, and high employment of movement verbs. in the same settings, @xcite claim that the basic assumption on deceptive language is that liars differ from truth-tellers in their verbal behavior, accordingly, lying stories may be identified by inspecting the verbal behavior patterns. in the same setting, newman (2003) proposes a set of linguistic behaviors, which, according to the authors, predict deception, such as tones of words, kind of preposition, conjunctions, and pronouns. a very plausible assumption would be that there are different discourse structure patterns in deceptive and truthful stories. indeed, this has been claimed by various authors @xcite @xcite @xcite . discourse-aware approaches for deception detection are usually framed as a supervised learning problem, which embodies in a model coherence relations followed by hierarchical nuclearity information to build automatic classifies. moreover, the discourse features may be also used as a complement for other stylistic-based linguistic features. therefore, discourse analysis applied to automated deception detection intends to assess the potential for the application of discourse coherence analysis, developing linguistic heuristics to automatically classify statements that are intentionally misstated (or manipulated). ## ongoing research in this ph.d. ongoing research, we are especially interested in discourse-aware approaches for deception detection, more specifically, using the rhetorical structure theory framework for fake news detection. our aim is to develop computational models for deception detection where discourse structure is a first-class citizen. we have already made progress toward this goal by: (1) developing the first multilingual deceptive corpus, which we called deceiver foot_0 , annotated by specialists according to the rst framework. deceiver multilingual corpus consists of 600 pieces of news classified in deceptive versus truthful documents, as well split in 300 documents in brazilian portuguese, and 300 documents in english; and (2) introducing two new rhetorical relations: interjection and imper-ative, which we consider to be relevant for the fake news detection task. moving forward, we propose to examine the discourse structure. our aim is to develop patterns that show deception. secondly, we will assess whether a computational model that embodies the multilingual discourse structure patterns is an efficient approach for multilingual fake news detection on the web and social media. therefore, the proposed research is divided among three main fronts described in what follows and shown in figure 2 : ## conclusions statements that are intentionally misstated (or manipulated) present linguistic features that are different in relation to the statements that are not intentionally misstated. according to @xcite , lying is saying something that you believe to be false with the intent to deceive, as well as deceptive language consists of a type of language deliberately used to attempt to mislead others @xcite . in recent years, there is a considerable increase in deceptive activities on the web and social media, including, for example, fake news, which consist in a global and relevant social, political, and economic problem. prior researches on fake news detection use a variety of linguistic styles and psychologist-based features for monolingual and multilingual factchecking systems. rather than using the classical linguistic style approach, recent research works propose successfully discourse-aware computational models for predicting online deception, more specifically, fake news and fake reviews. nevertheless, to the best of our knowledge, there are not any multilingual discourse-aware studies on deceptive texts. therefore, in this ph.d. ongoing research, we embrace the challenges and opportunities on the study of multilingual discourse structure in deceptive natural language texts for prediction of online deception, more specifically, fake news. we aim further to investigate discourse structure patterns from the first proposed multilingual deceptive corpus, annotated according to the rhetorical structure theory framework. then, we intend to build computational models that embody the discourse structure patterns that show deception, as well as to evaluate whether our approach is efficient for the multilingual fake news detection task. in this paper, we presented the ongoing ph.d. research and described the already in progress work toward this thesis's goals. ours research method includes (a) the construction of the first multilingual corpus of deceptive versus truthful stories (news) in portuguese and english languages, and (b) the introduction of two new rhetorical relations, which consist of the second contribution of this research. the proposed rhetorical relations, named inter-jection and imperative, present pragmatic information and are mononuclear. moreover, we suggest that these proposed relations are relevant for discourse-aware approaches for fake news detection. finally, we discuss the future research paths for further exploration related to this thesis's goals.