Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia

The recent COVID-19 pandemic has caused unprecedented impact across the globe. We have also witnessed millions of people with increased mental health issues, such as depression, stress, worry, fear, disgust, sadness, and anxiety, which have become one of the major public health concerns during this severe health crisis. For instance, depression is one of the most common mental health issues according to the findings made by the World Health Organisation (WHO). Depression can cause serious emotional, behavioural and physical health problems with significant consequences, both personal and social costs included. This paper studies community depression dynamics due to COVID-19 pandemic through user-generated content on Twitter. A new approach based on multi-modal features from tweets and Term Frequency-Inverse Document Frequency (TF-IDF) is proposed to build depression classification models. Multi-modal features capture depression cues from emotion, topic and domain-specific perspectives. We study the problem using recently scraped tweets from Twitter users emanating from the state of New South Wales in Australia. Our novel classification model is capable of extracting depression polarities which may be affected by COVID-19 and related events during the COVID-19 period. The results found that people became more depressed after the outbreak of COVID-19. The measures implemented by the government such as the state lockdown also increased depression levels. Further analysis in the Local Government Area (LGA) level found that the community depression level was different across different LGAs. Such granular level analysis of depression dynamics not only can help authorities such as governmental departments to take corresponding actions more objectively in specific regions if necessary but also allows users to perceive the dynamics of depression over the time

Suicide risk detection on social media using neural networks

Σύμφωνα με τον Παγκόσμιο Οργανισμό Υγείας, προσεγγιστικά 280 εκατομμύρια άνθρωποι υποφέρουν από κατάθληψη και πάνω από 700.000 αυτοκτονούν, ενώ η αυτοκτονία είναι η τέταρτη σε αριθμό θυμάτων αιτία θανάτου εφήβων και νέων ανθρώπων. Παρόλο που η κατάθλιψη επιδέχεται θεραπεία, η πλειοψηφία των νοσούντων αρνείται να αποδεχτεί ότι νοσεί και να ζητήσει ψυχιατρική βοήθεια, ειδικά στις αναπτυσσόμενες χώρες όπου υπάρχει κοινωνικό στίγμα όσον αφορά τις ψυχικές ασθένειες. Οι πλατφόρμες κοινωνικών δικτύων φιλοξενούν ανθρώπους από όλες τις δημογραφικές ομάδες με διαφορετικά χαρακτηριστικά και έχουν ιδιαίτερη απήχηση στους νέους. Για τους περισσότερους ανθρώπους τα κοινωνικά δίκτυα αποτελούν έναν ασφαλή χώρο όπου μπορούν να εκφράσουν τις σκέψεις και τις ανησυχίες τους, ειδικά όταν τους παρέχεται ανωνυμία. Πλατφόρμες όπως το Facebook και το Instagram έχουν δημιουργήσει επιλογή αναφοράς δημοσίευσης όπου χρήστες μπορούν να αναφέρουν μια δημοσίευση που υπονοεί αυτοκτονικές κινήσεις. Είναι υψίστης σημασίας αυτή η διαδικάσία να αυτοματοποιηθεί ούτως ώστε να μην παραβλέπονται δημοσιεύσεις με τέτοιο περιεχόμενο και επιπλέον να υπάρχει η δυνατότητα πρόβλεψης καταθλιπτικών τάσεων το νωρίτερο δυνατό. Σκοπός αυτής της εργασίας είναι η πρόταση και δημιουργία ενός μοντέλου που θα εκπαιδεύεται σε δημοσιεύσεις της πλατφόρμας κοινωνικών δικτύων Reddit και θα προβλέπει με αξιοπιστία αν ένας χρήστης εμφανίζει σημάδια κατάθλιψης εξετάζοντας της δημοσιεύσεις του. Το προτεινόμενο μοντέλο νευρωνικών δικτύων αποτελεί ένα υβριδικό μοντέλο που συνιστάται από συνελικτικά και αναδρομικά δίκτυα, καθώς και από έναν μηχανισμό προσοχής για τη μεγιστοποίηση της ακρίβειας των προβλέψεων.According to World Health Organization records, approximately 280 million people suffer from depression and over 700.000 people die due to suicide while suicide is the fourth leading cause of death among adolescents and young people. Whilst depression is a treatable condition, most people refuse to accept that they are affected and therefore seek psychiatric help, due to social stigma associated with mental disorders, especially in middle-income countries. Social media platforms host people of all kinds of demographic groups and characteristics and they thrive on young people. For most people social media set a safe space where they can share thoughts and concerns, especially when they are covered by anonymity. Platforms like Facebook and Instagram have created report options for such cases where users can report a post that implies suicidal actions. It is of high importance that this procedure becomes automated, so that no users in need slip our attention and also depressive tendencies can be predicted when it is still early. To resolve this problem, this thesis suggests a NN model that is trained on Reddit users’ posts and can reliably predict if a user shows depressive or suicidal signs by examining his posts. The suggested NN is a hybrid model that combines CNN and Bi-LSTM networks and also uses an attention mechanism to optimise predictions

AI for social good: social media mining of migration discourse

The number of international migrants has steadily increased over the years, and it has become one of the pressing issues in today’s globalized world. Our bibliometric review of around 400 articles on Scopus platform indicates an increased interest in migration-related research in recent times but the extant research is scattered at best. AI-based opinion mining research has predominantly noted negative sentiments across various social media platforms. Additionally, we note that prior studies have mostly considered social media data in the context of a particular event or a specific context. These studies offered a nuanced view of the societal opinions regarding that specific event, but this approach might miss the forest for the trees. Hence, this dissertation makes an attempt to go beyond simplistic opinion mining to identify various latent themes of migrant-related social media discourse. The first essay draws insights from the social psychology literature to investigate two facets of Twitter discourse, i.e., perceptions about migrants and behaviors toward migrants. We identified two prevailing perceptions (i.e., sympathy and antipathy) and two dominant behaviors (i.e., solidarity and animosity) of social media users toward migrants. Additionally, this essay has also fine-tuned the binary hate speech detection task, specifically in the context of migrants, by highlighting the granular differences between the perceptual and behavioral aspects of hate speech. The second essay investigates the journey of migrants or refugees from their home to the host country. We draw insights from Gennep's seminal book, i.e., Les Rites de Passage, to identify four phases of their journey: Arrival of Refugees, Temporal stay at Asylums, Rehabilitation, and Integration of Refugees into the host nation. We consider multimodal tweets for this essay. We find that our proposed theoretical framework was relevant for the 2022 Ukrainian refugee crisis – as a use-case. Our third essay points out that a limited sample of annotated data does not provide insights regarding the prevailing societal-level opinions. Hence, this essay employs unsupervised approaches on large-scale societal datasets to explore the prevailing societal-level sentiments on YouTube platform. Specifically, it probes whether negative comments about migrants get endorsed by other users. If yes, does it depend on who the migrants are – especially if they are cultural others? To address these questions, we consider two datasets: YouTube comments before the 2022 Ukrainian refugee crisis, and during the crisis. Second dataset confirms the Cultural Us hypothesis, and our findings are inconclusive for the first dataset. Our final or fourth essay probes social integration of migrants. The first part of this essay probed the unheard and faint voices of migrants to understand their struggle to settle down in the host economy. The second part of this chapter explored the viability of social media platforms as a viable alternative to expensive commercial job portals for vulnerable migrants. Finally, in our concluding chapter, we elucidated the potential of explainable AI, and briefly pointed out the inherent biases of transformer-based models in the context of migrant-related discourse. To sum up, the importance of migration was recognized as one of the essential topics in the United Nation’s Sustainable Development Goals (SDGs). Thus, this dissertation has attempted to make an incremental contribution to the AI for Social Good discourse

Cooperative Multimodal Approach to Depression Detection in Twitter

The advent of social media has presented a promising new opportunity for the early detection of depression. To do so effectively, there are two challenges to overcome. The first is that textual and visual information must be jointly considered to make accurate inferences about depression. The second challenge is that due to the variety of content types posted by users, it is difficult to extract many of the relevant indicator texts and images. In this work, we propose the use of a novel cooperative multi-agent model to address these challenges. From the historical posts of users, the proposed method can automatically select related indicator texts and images. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods by a large margin (over 30% error reduction). In several experiments and examples, we also verify that the selected posts can successfully indicate user depression, and our model can obtained a robust performance in realistic scenarios