Invisible Women in Digital Diplomacy: A Multidimensional Framework for Online Gender Bias Against Women Ambassadors Worldwide

Despite mounting evidence that women in foreign policy often bear the brunt of online hostility, the extent of online gender bias against diplomats remains unexplored. This paper offers the first global analysis of the treatment of women diplomats on social media. Introducing a multidimensional and multilingual methodology for studying online gender bias, it focuses on three critical elements: gendered language, negativity in tweets directed at diplomats, and the visibility of women diplomats. Our unique dataset encompasses ambassadors from 164 countries, their tweets, and the direct responses to these tweets in 65 different languages. Using automated content and sentiment analysis, our findings reveal a crucial gender bias. The language in responses to diplomatic tweets is only mildly gendered and largely pertains to international affairs and, generally, women ambassadors do not receive more negative reactions to their tweets than men, yet the pronounced discrepancy in online visibility stands out as a significant form of gender bias. Women receive a staggering 66.4% fewer retweets than men. By unraveling the invisibility that obscures women diplomats on social media, we hope to spark further research on online bias in international politics

What impacts matriculation decisions? A double-blind experiment via an AI-led chatbot trained with social media data

This thesis explores students’ matriculation decision factors via an AI-led chatbot trained with social media data. The novelty of this thesis resides in the following methodological approaches: Firstly, it employs data mining and text analytics techniques to explore the use of topic modelling and a systematic literature reviewing technique called algorithmic document sequencing to identify decision factors from social media to be integrated to the internal model of the AI through a methodological pluralist approach. Secondly, it introduces a chatbot design and strategy for an AI-led chat survey generating both unstructured qualitative and structured quantitative primary data. Finally, upon interviewing 1193 participants around the world, a double-blind true experiment was run seamlessly without human intervention by the AI testing hypotheses and determining the factors that impact students' university choices. The thesis showcases how AI can efficiently interview participants and collect their input, offering robust evidence through an RCT (Gold standard) to establish causal relationships between interventions and their outcomes. One significant contribution of the thesis lies in aiding higher education institutions in understanding the global factors influencing students' university choices and the role of electronic word-of-mouth on social media platforms. More importantly, the research enhances knowledge in identifying themes from social media and literature, facilitating the training of AI-augmented chatbots with these themes, and designing such chatbots to run large scale social RCTs. These developments may enable researchers from a wide range of fields to collect qualitative and quantitative data from large samples, run double-blind true experiments with the AI and produce statistically reproducible, reliable, and generalisable results