The Impact of Psycholinguistic Patterns in Discriminating between Fake News Spreaders and Fact Checkers

[EN] Fake news is a threat to society. A huge amount of fake news is posted every day on social networks which is read, believed and sometimes shared by a number of users. On the other hand, with the aim to raise awareness, some users share posts that debunk fake news by using information from fact-checking websites. In this paper, we are interested in exploring the role of various psycholinguistic characteristics in differentiating between users that tend to share fake news and users that tend to debunk them. Psycholinguistic characteristics represent the different linguistic information that can be used to profile users and can be extracted or inferred from users¿ posts. We present the CheckerOrSpreader model that uses a Convolution Neural Network (CNN) to differentiate between spreaders and checkers of fake news. The experimental results showed that CheckerOrSpreader is effective in classifying a user as a potential spreader or checker. Our analysis showed that checkers tend to use more positive language and a higher number of terms that show causality compared to spreaders who tend to use a higher amount of informal language, including slang and swear words.The works of Anastasia Giachanou and Daniel Oberski were funded by the Dutch Research Council (grant VI.Vidi.195.152). The work of Paolo Rosso was in the framework of the XAI-DisInfodemics project on eXplainable AI for disinformation and conspiracy detection during infodemics (PLEC2021-007681), funded by the Spanish Ministry of Science and Innovation, as well as IBERIFIER, the Iberian Digital Media Research and Fact-Checking Hub funded by the European Digital Media Observatory (2020-EU-IA0252).Giachanou, A.; Ghanem, BHH.; Rissola, EA.; Rosso, P.; Crestani, F.; Oberski, D. (2022). The Impact of Psycholinguistic Patterns in Discriminating between Fake News Spreaders and Fact Checkers. Data & Knowledge Engineering. 138:1-15. https://doi.org/10.1016/j.datak.2021.10196011513

Linguistic Alternatives to Quantitative Research Strategies Part One: How Linguistic Mechanisms Advance Research

Combining psycholinguistic technologies and systems analysis created advances in motivational profiling and numerous new behavioral engineering applications. These advances leapfrog many mainstream statistical research methods, producing superior research results via cause-effect language mechanisms. Entire industries explore motives ranging from opinion polling to persuasive marketing campaigns, and individual psychotherapy to executive performance coaching. Qualitative research tools such as questionnaires, interviews, and focus groups are now transforming static language data into dynamic linguistic systems measurement technology. Motivational mechanisms, especially linguistic mechanisms, allow specific changes within a motive’s operations. This includes both the choices the intervention creates and its end-goal. Predictable behavior changes are impossible with popular statistical methods. Advanced linguistic research strategies employ motivational change methods with state-of-the -art language and communications modeling

How Linguistic Frames Affect Motivational Profiles and the Roles of Quantitative versus Qualitative Research Strategies

The combined tools of psycholinguistics and systems analysis have produced advances in motivational profiling resulting in numerous applications to behavioral engineering. Knowing the way people frame their motive offers leverage in causing behavior change ranging from persuasive marketing campaigns, forensic profiling, individual psychotherapy, and executive performance. Professionals study motivation in applied or theoretical settings, often with strong implicit biases toward either quantitative or qualitative strategies. Many experts habitually frame behavioral research issues with ill-fitting quantitative and qualitative strategies. The third strategic choice offered here is state-of -the -art, psycholinguistic communications modeling. The role of these research strategies is explored

Linguistic Mechanisms Cause Rapid Behavior Change Part Two: How Linguistic Frames Affect Motivation

Written and spoken language contains inherent mechanisms driving motivation. Accessing and modifying psycholinguistic mechanisms, links language frames to changes in behavior within the context of motivational profiling. For example, holding an object like an imported apple feels safe until one is informed it was grown in a toxic waste dump. Instantly linguistic processing changes the apple’s meaning to dangerous. Qualitative data change from static into dynamic measures of motivational changes. Linguistic cause-effect mechanisms dramatically enhance the results and meaning of qualitative research methods, resulting new applications for behavioral engineering, including opinion polling, persuasive marketing campaigns, individual psychotherapy and executive performance coaching. Motivational mechanisms, especially linguistic frames, engineer deliberate and predictable improvements in outcomes, impossible with popular statistical methods

Linguistic Threat Assessment: Understanding Targeted Violence through Computational Linguistics

Language alluding to possible violence is widespread online, and security professionals are increasingly faced with the issue of understanding and mitigating this phenomenon. The volume of extremist and violent online data presents a workload that is unmanageable for traditional, manual threat assessment. Computational linguistics may be of particular relevance to understanding threats of grievance-fuelled targeted violence on a large scale. This thesis seeks to advance knowledge on the possibilities and pitfalls of threat assessment through automated linguistic analysis. Based on in-depth interviews with expert threat assessment practitioners, three areas of language are identified which can be leveraged for automation of threat assessment, namely, linguistic content, style, and trajectories. Implementations of each area are demonstrated in three subsequent quantitative chapters. First, linguistic content is utilised to develop the Grievance Dictionary, a psycholinguistic dictionary aimed at measuring concepts related to grievance-fuelled violence in text. Thereafter, linguistic content is supplemented with measures of linguistic style in order to examine the feasibility of author profiling (determining gender, age, and personality) in abusive texts. Lastly, linguistic trajectories are measured over time in order to assess the effect of an external event on an extremist movement. Collectively, the chapters in this thesis demonstrate that linguistic automation of threat assessment is indeed possible. The concluding chapter describes the limitations of the proposed approaches and illustrates where future potential lies to improve automated linguistic threat assessment. Ideally, developers of computational implementations for threat assessment strive for explainability and transparency. Furthermore, it is argued that computational linguistics holds particular promise for large-scale measurement of grievance-fuelled language, but is perhaps less suited to prediction of actual violent behaviour. Lastly, researchers and practitioners involved in threat assessment are urged to collaboratively and critically evaluate novel computational tools which may emerge in the future

Lexical coverage in ELF

The aim of this study was to determine how much vocabulary is needed to understand English in contexts where it is spoken internationally as a lingua franca (ELF). This information is critical to inform vocabulary size targets for second language (L2) learners of English. The current research consensus, based on native-English-speaker data, is that 6,000–7,000 word families plus proper nouns are needed. However, since English has become a global lingua franca, native speakers of English have become a minority: in fact, today, there are around two billion speakers of English worldwide, of which less than a quarter are native speakers. This means that non-native speakers of English are more likely to interact with other non-native speakers than with native speakers. Thus, using findings based on solely native-speaker data may not provide the most accurate information needed to inform vocabulary size targets for L2 learners of English. Indeed, this information needs to be supplemented with data from competent non-native speakers of English who can represent a legitimate model for L2 learners of English. This study uses the largest freely available corpus of general, spoken ELF in Europe: the one million-word Vienna-Oxford International Corpus of English (VOICE). The word family was used as a lexical counting unit, and the lexical coverage of VOICE was calculated for various thresholds of the most frequent word families in the corpus. A comparative analysis was carried out to determine the lexical coverage of VOICE provided by frequency ranked word lists based on data from the British National Corpus of English and the Contemporary Corpus of American English. The main findings of this study indicate that fewer than 3,000–4,000 word families plus proper nouns can provide the lexical resources needed to understand English in international contexts where it is spoken as a lingua franca. This is approximately half the number of word families (i.e. 6,000–7,000 word families plus proper nouns) which scholars have claimed are needed to understand spoken English. The findings of this study represent a substantial saving in vocabulary size targets for L2 learners of English who wish to be functional in understanding English spoken as an international lingua franca

Detecting child grooming behaviour patterns on social media

Online paedophile activity in social media has become a major concern in society as Internet access is easily available to a broader younger population. One common form of online child exploitation is child grooming, where adults and minors exchange sexual text and media via social media platforms. Such behaviour involves a number of stages performed by a predator (adult) with the final goal of approaching a victim (minor) in person. This paper presents a study of such online grooming stages from a machine learning perspective. We propose to characterise such stages by a series of features covering sentiment polarity, content, and psycho-linguistic and discourse patterns. Our experiments with online chatroom conversations show good results in automatically classifying chatlines into various grooming stages. Such a deeper understanding and tracking of predatory behaviour is vital for building robust systems for detecting grooming conversations and potential predators on social media

Author Profiling for English and Arabic Emails

This paper reports on some aspects of a research project aimed at automating the analysis of texts for the purpose of author profiling and identification. The Text Attribution Tool (TAT) was developed for the purpose of language-independent author profiling and has now been trained on two email corpora, English and Arabic. The complete analysis provides probabilities for the author’s basic demographic traits (gender, age, geographic origin, level of education and native language) as well as for five psychometric traits. The prototype system also provides a probability of a match with other texts, whether from known or unknown authors. A very important part of the project was the data collection and we give an overview of the collection process as well as a detailed description of the corpus of email data which was collected. We describe the overall TAT system and its components before outlining the ways in which the email data is processed and analysed. Because Arabic presents particular challenges for NLP, this paper also describes more specifically the text processing components developed to handle Arabic emails. Finally, we describe the Machine Learning setup used to produce classifiers for the different author traits and we present the experimental results, which are promising for most traits examined.The work presented in this paper was carried out while the authors were working at Appen Pty Ltd., Chatswood NSW 2067, Australi

Author Profiling for English and Arabic Emails

This paper reports on some aspects of a research project aimed at automating the analysis of texts for the purpose of author profiling and identification. The Text Attribution Tool (TAT) was developed for the purpose of language-independent author profiling and has now been trained on two email corpora, English and Arabic. The complete analysis provides probabilities for the author’s basic demographic traits (gender, age, geographic origin, level of education and native language) as well as for five psychometric traits. The prototype system also provides a probability of a match with other texts, whether from known or unknown authors. A very important part of the project was the data collection and we give an overview of the collection process as well as a detailed description of the corpus of email data which was collected. We describe the overall TAT system and its components before outlining the ways in which the email data is processed and analysed. Because Arabic presents particular challenges for NLP, this paper also describes more specifically the text processing components developed to handle Arabic emails. Finally, we describe the Machine Learning setup used to produce classifiers for the different author traits and we present the experimental results, which are promising for most traits examined.The work presented in this paper was carried out while the authors were working at Appen Pty Ltd., Chatswood NSW 2067, Australi