Personality Recognition in Conversations using Capsule Neural Networks

Lecture Notes in Computer Science

Ghanem

et al. 2020

Self Cite

Users play a critical role in the creation and propagation of fake news online by consuming and sharing articles with inaccurate information either intentionally or unintentionally. Fake news are written in a way to confuse readers and therefore understanding which articles contain fabricated information is very challenging for non-experts. Given the difficulty of the task, several fact checking websites have been developed to raise awareness about which articles contain fabricated information. As a result of those platforms, several users are interested to share posts that cite evidence with the aim to refute fake news and warn other users. These users are known as fact checkers. However, there are users who tend to share false information, who can be characterised as potential fake news spreaders. In this paper, we propose the CheckerOrSpreader model that can classify a user as a potential fact checker or a potential fake news spreader. Our model is based on a Convolutional Neural Network (CNN) and combines word embeddings with features that represent users' personality traits and linguistic patterns used in their tweets. Experimental results show that leveraging linguistic patterns and personality traits can improve the performance in differentiating between checkers and spreaders.

Section: Related Workmentioning

confidence: 99%

The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers

Giachanou

Lecture Notes in Computer Science

Ghanem

et al. 2020

Self Cite

“…Research on language and psychology has shown that various useful cues about an individuals' mental state (as well as personality, social and emotional conditions) can be discovered by examining the patterns of their language use [6]. As a matter of fact, language attributes could act as indicators of the current mental state [22,25], personality [19,26] and even personal values [2,4]. The main reason, as argued by Pennebaker et al [21], is because such latent mental-related variables are encoded in the words that individuals use to communicate.…”

Section: Introductionmentioning

confidence: 99%

Beyond Modelling: Understanding Mental Disorders in Online Social Media

Lecture Notes in Computer Science

Aliannejadi²,

Crestani

2020

Self Cite

Mental disorders are a major concern in societies all over the world, and in spite of the improved diagnosis rates of such disorders in recent years, many cases still go undetected. Nowadays, many people are increasingly utilising online social media platforms to share their feelings and moods. Despite the collective efforts in the community to develop models for identifying potential cases of mental disorders, not much work has been done to provide insights that could be used by a predictive system or a health practitioner in the elaboration of a diagnosis.In this paper, we present our research towards better visualising and understanding the factors that characterise and differentiate social media users who are affected by mental disorders from those who are not. Furthermore, we study to which extent various mental disorders, such as depression and anorexia, differ in terms of language use. We conduct different experiments considering various dimensions of language such as vocabulary, psychometric attributes and emotional indicators. Our findings reveal that positive instances of mental disorders show significant differences from control individuals in the way they write and express emotions in social media. However, there are not quantifiable differences that could be used to distinguish one mental disorder from each other.Work done while Mohammad Aliannejadi was affiliated with Università della Svizzera italiana (USI).

“…Since then, the problem of conversational systems has been studied by researchers from both the fields of IR and Natural Language Processing (NLP) with varied interests. Conversational Agents have forayed their applications in various domains ranging from conversational recommender systems [11,34], human memory augmentation [6], e-Health systems [25], personality recognition [31] to museum tour guidance [22]. Gao et al [17] provides a systematic review on neural approaches to conversational AI developed in the last few years.…”

Section: Related Workmentioning

confidence: 99%

Harnessing Evolution of Multi-Turn Conversations for Effective Answer Retrieval

Aliannejadi

Chakraborty

Proceedings of the 2020 Conference on Human Information Interaction and Retrieval

et al. 2020

Self Cite

With the improvements in speech recognition and voice generation technologies over the last years, a lot of companies have sought to develop conversation understanding systems that run on mobile phones or smart home devices through natural language interfaces. Conversational assistants, such as Google Assistant™ and Microsoft Cortana™, can help users to complete various types of tasks. This requires an accurate understanding of the user's information need as the conversation evolves into multiple turns. Finding relevant context in a conversation's history is challenging because of the complexity of natural language and the evolution of a user's information need. In this work, we present an extensive analysis of language, relevance, dependency of user utterances in a multi-turn information-seeking conversation. To this aim, we have annotated relevant utterances in the conversations released by the TREC CaST 2019 track. The annotation labels determine which of the previous utterances in a conversation can be used to improve the current one. Furthermore, we propose a neural utterance relevance model based on BERT fine-tuning, outperforming competitive baselines. We study and compare the performance of multiple retrieval models, utilizing different strategies to incorporate the user's context. The experimental results on both classification and retrieval tasks show that our proposed approach can effectively identify and incorporate the conversation context. We show that processing the current utterance using the predicted relevant utterance leads to a 38% relative improvement in terms of nDCG@20. Finally, to foster research in this area, we have released the dataset of the annotations.