4 citations · 11 across the 17 of their papers we have counts for
17 papers
Epistemic orientation predicts legislative effectiveness among members of the US Congress
Segun Aroyehun, Stephan Lewandowsky, David Garcia
Truth and evidence-based communication provide important foundations for democratic governance, accountability, and collective decision-making. Prior work shows that evidence-orien…
Politics of Feelings: Emotional Expression and Legislative Effectiveness in the U.S. Congress
Segun Aroyehun
Emotions are a pervasive feature of political communication, yet existing research has focused primarily on describing patterns of emotional expression rather than examining whethe…
German parties shifted towards intuition-based rhetoric after the far right's parliamentary breakthrough
Peer Saleth, Segun T. Aroyehun, Fabio Carrella +3
The spread of misinformation is widely perceived as a threat to democratic deliberation, yet how political elites' rhetorical commitments to truth shift alongside the rise of popul…
Anticipating Innovation Using Large Language Models
Enrico Maria Fenoaltea, Filippo Santoro, Giordano De Marzo +2
Forecasting innovation, intended as the emergence of new technological combinations, is a fundamental challenge for science and policy. We show that forthcoming combinations leave…
Epistemic orientation in parliamentary discourse is associated with deliberative democracy
Segun Aroyehun, Stephan Lewandowsky, David Garcia
The pursuit of truth is central to democratic deliberation and governance, yet political discourse reflects varying epistemic orientations, ranging from evidence-based reasoning gr…
Automatic Classifiers Underdetect Emotions Expressed by Men
Ivan Smirnov, Segun T. Aroyehun, Paul Plener +1
The widespread adoption of automatic sentiment and emotion classifiers makes it important to ensure that these tools perform reliably across different populations. Yet their reliab…
Large Language Models Approach Expert Pedagogical Quality in Math Tutoring but Differ in Instructional and Linguistic Profiles
Ramatu Oiza Abdulsalam, Segun Aroyehun
Recent work has explored the use of large language models (LLMs) to generate tutoring responses in mathematics, yet it remains unclear how closely their instructional behavior alig…
Linguistic and Embedding-Based Profiling of Texts generated by Humans and Large Language Models
Sergio E. Zanotto, Segun Aroyehun
The rapid advancements in large language models (LLMs) have significantly improved their ability to generate natural language, making texts generated by LLMs increasingly indisting…
Human Variability vs. Machine Consistency: A Linguistic Analysis of Texts Generated by Humans and Large Language Models
Sergio E. Zanotto, Segun Aroyehun
The rapid advancements in large language models (LLMs) have significantly improved their ability to generate natural language, making texts generated by LLMs increasingly indisting…
A multitask learning framework for leveraging subjectivity of annotators to identify misogyny
Jason Angel, Segun Taofeek Aroyehun, Grigori Sidorov +1
Identifying misogyny using artificial intelligence is a form of combating online toxicity against women. However, the subjective nature of interpreting misogyny poses a significant…
Computational analysis of US Congressional speeches reveals a shift from evidence to intuition
Segun Taofeek Aroyehun, Almog Simchon, Fabio Carrella +3
Pursuit of honest and truthful decision-making is crucial for governance and accountability in democracies. However, people sometimes take different perspectives of what it means t…
LEIA: Linguistic Embeddings for the Identification of Affect
Segun Taofeek Aroyehun, Lukas Malik, Hannah Metzler +3
The wealth of text data generated by social media has enabled new kinds of analysis of emotions with language models. These models are often trained on small and costly datasets of…
Collective moderation of hate, toxicity, and extremity in online discussions
Jana Lasser, Alina Herderich, Joshua Garland +3
In the digital age, hate speech poses a threat to the functioning of social media platforms as spaces for public discourse. Top-down approaches to moderate hate speech encounter di…
From alternative conceptions of honesty to alternative facts in communications by U.S. politicians
Jana Lasser, Segun Taofeek Aroyehun, Fabio Carrella +3
The spread of online misinformation on social media is increasingly perceived as a problem for societal cohesion and democracy. The role of political leaders in this process has at…
Social media sharing by political elites: An asymmetric American exceptionalism
Jana Lasser, Segun Taofeek Aroyehun, Almog Simchon +3
Increased sharing of untrustworthy information on social media platforms is one of the main challenges of our modern information society. Because information disseminated by politi…
NLP-CIC @ PRELEARN: Mastering prerequisites relations, from handcrafted features to embeddings
Jason Angel, Segun Taofeek Aroyehun, Alexander Gelbukh
We present our systems and findings for the prerequisite relation learning task (PRELEARN) at EVALITA 2020. The task aims to classify whether a pair of concepts hold a prerequisite…
NLP-CIC at SemEval-2020 Task 9: Analysing sentiment in code-switching language using a simple deep-learning classifier
Jason Angel, Segun Taofeek Aroyehun, Antonio Tamayo +1
Code-switching is a phenomenon in which two or more languages are used in the same message. Nowadays, it is quite common to find messages with languages mixed in social media. This…