Incorporating Emotions into Health Mention Classification Task on Social Media

The health mention classification (HMC) task is the process of identifying and classifying mentions of health-related concepts in text. This can be useful for identifying and tracking the spread of diseases through social media posts. However, this is a non-trivial task. Here we build on recent studies suggesting that using emotional information may improve upon this task. Our study results in a framework for health mention classification that incorporates affective features. We present two methods, an intermediate task fine-tuning approach (implicit) and a multi-feature fusion approach (explicit) to incorporate emotions into our target task of HMC. We evaluated our approach on 5 HMC-related datasets from different social media platforms including three from Twitter, one from Reddit and another from a combination of social media sources. Extensive experiments demonstrate that our approach results in statistically significant performance gains on HMC tasks. By using the multi-feature fusion approach, we achieve at least a 3% improvement in F1 score over BERT baselines across all datasets. We also show that considering only negative emotions does not significantly affect performance on the HMC task. Additionally, our results indicate that HMC models infused with emotional knowledge are an effective alternative, especially when other HMC datasets are unavailable for domain-specific fine-tuning. The source code for our models is freely available at https://github.com/tahirlanre/Emotion_PHM

Hos in the garden: staging and resisting neoliberal creativity

This article takes up the challenge of extending and enhancing the literature on arts interventions and creative city policies by considering the role of feminist and queer artistic praxis in contemporary urban politics. Here I reflect on the complicities and potentialities of two Toronto-based arts interventions: Dig In and the Dirty Plotz cabaret. I analyse an example of community based arts strategy that strived to ‘revitalise’ one disinvested Toronto neighbourhood. I also reflect on my experience performing drag king urban planner, Toby Sharp. Reflecting on these examples, I show how market-oriented arts policies entangle women artists in the cultivation of spaces of depoliticised feminism, homonormativity and white privilege. However, I also demonstrate how women artists are playfully and performatively pushing back at hegemonic regimes with the radical aesthetic praxis of cabaret. I maintain that bringing critical feminist arts spaces and cabaret practice into discussions about neoliberal urban policies uncovers sites of feminist resistance and solidarity, interventions that challenge violent processes of colonisation and privatisation on multiple fronts

Psychological Manipulation in Political Discourse: A Case Study of Facebook Posts on Urhobo Language Curriculum

Texts, either written or spoken, are of varying types and serve different functions, including serving as a means of socially influencing people through underhanded tactics. In the present study, patterns of how the language used in social media-based political discourse reflects psychological manipulation tendencies on the part of netizens are examined. The goal is to investigate the psychological manipulation types and functions embedded in the texts. The study draws on insights from the perceived role that Facebook posts, comments, replies, and reactions had in the approval of the Urhobo Language Curriculum (UCL). The data for the study was collected from Facebook posts on the subject matter of the Urhobo language curriculum (ULC) made between 2015 and 2016. The discussion in the study is descriptive and leans on inference from addresses’ (evaluative) responses to the identified posts. These responses include comments, replies, and reactions. The approach adopted is based on the assumption that “... the perlocutionary effect of the addresser’s statement is represented in the addressee’s statement” (Boboshko 2015:64). It is argued that the texts used in social-media-based political discourses serve two functions: (i) informing; and (ii) brandishing emotions with the intention of manipulating a target into doing what one wants

Social Media, Digital Activism, and Online Collective Action:_x000D_ A Tale of Two Overlapping Women\u27s Rights Movements

Research on collective action (CA) dates back to at least the 1960s. However, the plethora of Internet-driven CAs warrants the need to revisit the theory of CA. By analyzing blog and Twitter postings for the two movements, “Women to Drive” and “Sexual Harassment”, we - (1) develop novel methodologies to model online CAs by utilizing existing CA theories and computational approaches for social network analysis, sentiment analysis, text mining, and content analysis, (2) establish a rigorous and fundamental analytical framework to understand the emergence, evolution, development and trajectory of CAs in complex online environments, and (3) study coalition formation, interorganizational communication, and transnational support of the two online CAs. The study also identifies cross-cultural aspects of the campaign network, where Arabic hashtags relate to the local factors and English hashtags connect with transnational and interorganizational support from various organizations such as human rights and women’s rights

Challenging Journalistic Authority in the Networked Affective Dynamics of #Chemnitz

Journalism as an institution is increasingly under pressure in hybrid media systems. Various far-right actors use social media platforms as a key staging ground for contesting legacy media. Drawing on affect theory and discursive institutionalism, this article empirically examines how journalistic authority was challenged on Twitter during far-right riots in the German city of Chemnitz in 2018. Through these public and networked contestations, we see the emergence of “affective publics” that form around shared and competing emotions. Through social network analysis, we examine the networked polarization around #Chemnitz. By applying in-depth textual analysis, we then untangle how far-right actors attack legacy media by strategically mobilizing and performing outrage. Based on our findings, we propose to understand journalism as an affective institution, whose authority is perpetually contested as affective publics gain agency. Such an understanding involves a profound questioning of the notion of objectivity that has been constitutive of journalism in the 20th century

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