Social Media Analysis for Social Good

Data on social media is abundant and offers valuable information that can be utilised for a range of purposes. Users share their experiences and opinions on various topics, ranging from their personal life to the community and the world, in real-time. In comparison to conventional data sources, social media is cost-effective to obtain, is up-to-date and reaches a larger audience. By analysing this rich data source, it can contribute to solving societal issues and promote social impact in an equitable manner. In this thesis, I present my research in exploring innovative applications using \ac{NLP} and machine learning to identify patterns and extract actionable insights from social media data to ultimately make a positive impact on society. First, I evaluate the impact of an intervention program aimed at promoting inclusive and equitable learning opportunities for underrepresented communities using social media data. Second, I develop EmoBERT, an emotion-based variant of the BERT model, for detecting fine-grained emotions to gauge the well-being of a population during significant disease outbreaks. Third, to improve public health surveillance on social media, I demonstrate how emotions expressed in social media posts can be incorporated into health mention classification using an intermediate task fine-tuning and multi-feature fusion approach. I also propose a multi-task learning framework to model the literal meanings of disease and symptom words to enhance the classification of health mentions. Fourth, I create a new health mention dataset to address the imbalance in health data availability between developing and developed countries, providing a benchmark alternative to the traditional standards used in digital health research. Finally, I leverage the power of pretrained language models to analyse religious activities, recognised as social determinants of health, during disease outbreaks

Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring

How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal