arXiv subjects

Gionnieve Lim

Publications and source records attributed to Gionnieve Lim.

11 recordsLinked to original sources

arXiv2025

Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation

The emergence of Large Language Models (LLMs) presents a dual challenge in the fight against disinformation. These powerful tools, capable of generating human-like text at scale, can be weaponised to produce sophisticated and persuasive disinformation, yet they also hold promise for enhancing detection and mitigation strategies. This paper investigates the complex dynamics between LLMs and disinformation through a communication game that simulates online forums, inspired by the game Werewolf, with 25 participants. We analyse how Disinformers, Moderators, and Users leverage LLMs to advance their goals, revealing both the potential for misuse and combating disinformation. Our findings highlight the varying uses of LLMs depending on the participants' roles and strategies, underscoring the importance of understanding their effectiveness in this context. We conclude by discussing implications for future LLM development and online platform design, advocating for a balanced approach that empowers users and fosters trust while mitigating the risks of LLM-assisted disinformation.

cs.HC↗

arXiv2025

Iffy-Or-Not: Extending the Web to Support the Critical Evaluation of Fallacious Texts

Social platforms have expanded opportunities for deliberation with the comments being used to inform one's opinion. However, using such information to form opinions is challenged by unsubstantiated or false content. To enhance the quality of opinion formation and potentially confer resistance to misinformation, we developed Iffy-Or-Not (ION), a browser extension that seeks to invoke critical thinking when reading texts. With three features guided by argumentation theory, ION highlights fallacious content, suggests diverse queries to probe them with, and offers deeper questions to consider and chat with others about. From a user study (N=18), we found that ION encourages users to be more attentive to the content, suggests queries that align with or are preferable to their own, and poses thought-provoking questions that expands their perspectives. However, some participants expressed aversion to ION due to misalignments with their information goals and thinking predispositions. Potential backfiring effects with ION are discussed.

cs.HC↗

arXiv2024

XAI in Automated Fact-Checking? The Benefits Are Modest and There's No One-Explanation-Fits-All

The massive volume of online information along with the issue of misinformation has spurred active research in the automation of fact-checking. Like fact-checking by human experts, it is not enough for an automated fact-checker to just be accurate, but also be able to inform and convince the user of the validity of its predictions. This becomes viable with explainable artificial intelligence (XAI). In this work, we conduct a study of XAI fact-checkers involving 180 participants to determine how users' actions towards news and their attitudes towards explanations are affected by the XAI. Our results suggest that XAI has limited effects on users' agreement with the veracity prediction of the automated fact-checker and on their intent to share news. However, XAI nudges users towards forming uniform judgments of news veracity, thereby signaling their reliance on the explanations. We also found polarizing preferences towards XAI and raise several design considerations on them.

cs.HC↗

arXiv2024

Evaluation of an LLM in Identifying Logical Fallacies: A Call for Rigor When Adopting LLMs in HCI Research

There is increasing interest in the adoption of LLMs in HCI research. However, LLMs may often be regarded as a panacea because of their powerful capabilities with an accompanying oversight on whether they are suitable for their intended tasks. We contend that LLMs should be adopted in a critical manner following rigorous evaluation. Accordingly, we present the evaluation of an LLM in identifying logical fallacies that will form part of a digital misinformation intervention. By comparing to a labeled dataset, we found that GPT-4 achieves an accuracy of 0.79, and for our intended use case that excludes invalid or unidentified instances, an accuracy of 0.90. This gives us the confidence to proceed with the application of the LLM while keeping in mind the areas where it still falls short. The paper describes our evaluation approach, results and reflections on the use of the LLM for our intended task.

cs.HC↗

arXiv2024

Local Perceptions and Practices of News Sharing and Fake News

Fake news is a prevalent problem, particularly in digital media, that undermines trust and cooperation among people. As a variety of global mitigation efforts arise, the understanding of how people consider fake news becomes important, especially in local contexts. To that end, we carried out a survey with 75 participants in Singapore to understand people's perceptions of and practices with news (real and fake). Locally, fake news was found to be more pervasive in instant messaging apps than in social media, with the problem attributed more strongly to sharing than to creation. Good news sharing practices were generally observed. Highest trust was reported in government communication platforms across 11 media items. These results show that Singapore possesses a peculiar sociocultural scene, suggesting that efforts directed towards locally relevant measures may be more effective in addressing fake news in Singapore. We detail the survey results and recommended directions in this paper.

cs.HC↗

arXiv2024

Rapid AIdeation: Generating Ideas With the Self and in Collaboration With Large Language Models

Generative artificial intelligence (GenAI) can rapidly produce large and diverse volumes of content. This lends to it a quality of creativity which can be empowering in the early stages of design. In seeking to understand how creative ways to address practical issues can be conceived between humans and GenAI, we conducted a rapid ideation workshop with 21 participants where they used a large language model (LLM) to brainstorm potential solutions and evaluate them. We found that the LLM produced a greater variety of ideas that were of high quality, though not necessarily of higher quality than human-generated ideas. Participants typically prompted in a straightforward manner with concise instructions. We also observed two collaborative dynamics with the LLM fulfilling a consulting role or an assisting role depending on the goals of the users. Notably, we observed an atypical anti-collaboration dynamic where participants used an antagonistic approach to prompt the LLM.

cs.HC↗

arXiv2024

Fact Checking Chatbot: A Misinformation Intervention for Instant Messaging Apps and an Analysis of Trust in the Fact Checkers

In Singapore, there has been a rise in misinformation on mobile instant messaging services (MIMS). MIMS support both small peer-to-peer networks and large groups. Misinformation in the former may spread due to recipients' trust in the sender while in the latter, misinformation can directly reach a wide audience. The encryption of MIMS makes it difficult to address misinformation directly. As such, chatbots have become an alternative solution where users can disclose their chat content directly to fact checking services. To understand how effective fact checking chatbots are as an intervention and how trust in three different fact checkers (i.e., Government, News Outlets, and Artificial Intelligence) may affect this trust, we conducted a within-subjects experiment with 527 Singapore residents. We found mixed results for the fact checkers but support for the chatbot intervention overall. We also found a striking contradiction between participants' trust in the fact checkers and their behaviour towards them. Specifically, those who reported a high level of trust in the government performed worse and tended to follow the fact checking tool less when it was endorsed by the government.

cs.HC↗

arXiv2024

Effects of Automated Misinformation Warning Labels on the Intents to Like, Comment and Share Posts

With fact-checking by professionals being difficult to scale on social media, algorithmic techniques have been considered. However, it is uncertain how the public may react to labels by automated fact-checkers. In this study, we investigate the use of automated warning labels derived from misinformation detection literature and investigate their effects on three forms of post engagement. Focusing on political posts, we also consider how partisanship affects engagement. In a two-phases within-subjects experiment with 200 participants, we found that the generic warnings suppressed intents to comment on and share posts, but not on the intent to like them. Furthermore, when different reasons for the labels were provided, their effects on post engagement were inconsistent, suggesting that the reasons could have undesirably motivated engagement instead. Partisanship effects were observed across the labels with higher engagement for politically congruent posts. We discuss the implications on the design and use of automated warning labels.

cs.HC↗

arXiv2024

CollabCoder: A Lower-barrier, Rigorous Workflow for Inductive Collaborative Qualitative Analysis with Large Language Models

Collaborative Qualitative Analysis (CQA) can enhance qualitative analysis rigor and depth by incorporating varied viewpoints. Nevertheless, ensuring a rigorous CQA procedure itself can be both demanding and costly. To lower this bar, we take a theoretical perspective to design the CollabCoder workflow, that integrates Large Language Models (LLMs) into key inductive CQA stages: independent open coding, iterative discussions, and final codebook creation. In the open coding phase, CollabCoder offers AI-generated code suggestions and records decision-making data. During discussions, it promotes mutual understanding by sharing this data within the coding team and using quantitative metrics to identify coding (dis)agreements, aiding in consensus-building. In the code grouping stage, CollabCoder provides primary code group suggestions, lightening the cognitive load of finalizing the codebook. A 16-user evaluation confirmed the effectiveness of CollabCoder, demonstrating its advantages over existing software and providing empirical insights into the role of LLMs in the CQA practice.

cs.HC↗

arXiv2024

Help Me Reflect: Leveraging Self-Reflection Interface Nudges to Enhance Deliberativeness on Online Deliberation Platforms

The deliberative potential of online platforms has been widely examined. However, little is known about how various interface-based reflection nudges impact the quality of deliberation. This paper presents two user studies with 12 and 120 participants, respectively, to investigate the impacts of different reflective nudges on the quality of deliberation. In the first study, we examined five distinct reflective nudges: persona, temporal prompts, analogies and metaphors, cultural prompts and storytelling. Persona, temporal prompts, and storytelling emerged as the preferred nudges for implementation on online deliberation platforms. In the second study, we assess the impacts of these preferred reflectors more thoroughly. Results revealed a significant positive impact of these reflectors on deliberative quality. Specifically, persona promotes a deliberative environment for balanced and opinionated viewpoints while temporal prompts promote more individualised viewpoints. Our findings suggest that the choice of reflectors can significantly influence the dynamics and shape the nature of online discussions.

cs.HC↗

arXiv2022

A Review of Research on Civic Technology: Definitions, Theories, History and Insights

There have been initiatives that take advantage of information and communication technologies to serve civic purposes, referred to as civic technologies (Civic Tech). In this paper, we present a review of 224 papers from the ACM Digital Library focusing on Computer Supported Cooperative Work and Human-Computer Interaction, the key fields supporting the building of Civic Tech. Through this review, we discuss the concepts, theories and history of civic tech research and provide insights on the technological tools, social processes and participation mechanisms involved. Our work seeks to direct future civic tech efforts to the phase of by the citizens.

cs.CY↗