Charitha Elvitigala

Thushari Atapattu, Mahen Herath, Charitha Elvitigala et al.

People often utilise online media (e.g., Facebook, Reddit) as a platform to express their psychological distress and seek support. State-of-the-art NLP techniques demonstrate strong potential to automatically detect mental health issues from text. Research suggests that mental health issues are reflected in emotions (e.g., sadness) indicated in a person's choice of language. Therefore, we developed a novel emotion-annotated mental health corpus (EmoMent), consisting of 2802 Facebook posts (14845 sentences) extracted from two South Asian countries - Sri Lanka and India. Three clinical psychology postgraduates were involved in annotating these posts into eight categories, including 'mental illness' (e.g., depression) and emotions (e.g., 'sadness', 'anger'). EmoMent corpus achieved 'very good' inter-annotator agreement of 98.3% (i.e. % with two or more agreement) and Fleiss' Kappa of 0.82. Our RoBERTa based models achieved an F1 score of 0.76 and a macro-averaged F1 score of 0.77 for the first task (i.e. predicting a mental health condition from a post) and the second task (i.e. extent of association of relevant posts with the categories defined in our taxonomy), respectively.

Yazan Boshmaf, Charitha Elvitigala, Husam Al Jawaheri et al.

Cybercriminals exploit cryptocurrencies to carry out illicit activities. In this paper, we focus on Ponzi schemes that operate on Bitcoin and perform an in-depth analysis of MMM, one of the oldest and most popular Ponzi schemes. Based on 423K transactions involving 16K addresses, we show that: (1) Starting Sep 2014, the scheme goes through three phases over three years. At its peak, MMM circulated more than 150M dollars a day, after which it collapsed by the end of Jun 2016. (2) There is a high income inequality between MMM members, with the daily Gini index reaching more than 0.9. The scheme also exhibits a zero-sum investment model, in which one member's loss is another member's gain. The percentage of victims who never made any profit has grown from 0% to 41% in five months, during which the top-earning scammer has made 765K dollars in profit. (3) The scheme has a global reach with 80 different member countries but a highly-asymmetrical flow of money between them. While India and Indonesia have the largest pairwise flow in MMM, members in Indonesia have received 12x more money than they have sent to their counterparts in India.