| Challenge: | a study aimed to determine if people who are influential in online discussions retain influence when placed in a topic that is less familiar or perhaps not as interesting. |
| Approach: | They conducted a study to determine if people who are highly influential retain influence when moving to a topic that is less familiar or perhaps not as interesting. |
| Outcome: | The results show that people who are highly influential in group discussions lose influence when placed in a topic that is less familiar or perhaps not as interesting. |
Similar Papers
Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing research focuses on task-oriented or open-domain dialogue systems with influence skills. |
| Approach: | They propose to define and introduce a category of social influence dialogue systems that influence users’ cognitive and emotional responses. |
| Outcome: | The proposed system is task-oriented or goal-oriented, but it is not open-domain. |
Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models (2020.coling-main)
Copied to clipboard
| Challenge: | a study examines the impact of political ideology biases in training data . topic detection methods may contain or propagate certain biase resulting in a skewed data collection . |
| Approach: | They propose to learn a text representation that is invariant to political ideology while still judging topic relevance. |
| Outcome: | The proposed model can be invariant to political ideology while still judging topic relevance. |
Joint Effects of Context and User History for Predicting Online Conversation Re-entries (P19-1)
Copied to clipboard
| Challenge: | Existing methods for predicting online conversation re-entry focus on modeling engagement patterns in ongoing conversations or ignoring the rich information in users' previous chatting history. |
| Approach: | They propose a neural framework with three main layers to model the conversation context and user history and their interactions with Twitter and Reddit to predict whether a user will return to a conversation they once participated in. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two large-scale Twitter and Reddit conversations, and achieves an F1 score of 61.1 on Twitter conversations. |
A Similarity Measure for Comparing Conversational Dynamics (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Qualities of a conversation are dependent on how interactions combine to form a “shape” of the conversation. |
| Approach: | They propose a similarity measure to capture differences in conversation dynamics and assess its sensitivity to the topic of the conversation. |
| Outcome: | The proposed measure captures differences in conversation dynamics and assesses its sensitivity to the topic of the conversation. |
Dynamic Online Conversation Recommendation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing models that assume static user interests are unable to capture the temporal aspects of user interactions and interest changes over time. |
| Approach: | They propose a neural architecture to exploit changes of user interactions and interests over time to predict which discussions they are likely to enter. |
| Outcome: | The proposed model outperforms state-of-the-art models that assume static user interests and handle future conversations that are unseen during training time. |
Do Influence Functions Work on Large Language Models? (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Influence functions are important for quantifying the impact of individual training data points on a model’s predictions. |
| Approach: | They conduct a systematic study to address a key question: do influence functions work on large language models? |
| Outcome: | The influence functions perform poorly across multiple tasks and are therefore unsuitable for large language models. |
Social Convos: Capturing Agendas and Emotions on Social Media (2024.lrec-main)
Copied to clipboard
| Challenge: | Social media traffic can provide valuable insights into prevailing opinions and social dynamics among different segments of the population. |
| Approach: | They propose a method to extract influence indicators from messages circulating among groups . they build upon the concept of a convo to identify influential authors . |
| Outcome: | The proposed approach extracts influence indicators from messages circulating among groups of users discussing particular topics. |
Insights into using temporal coordinated behaviour to explore connections between social media posts and influence (2025.findings-emnlp)
Copied to clipboard
Elisa Sartori, Serena Tardelli, Maurizio Tesconi, Mauro Conti, Alessandro Galeazzi, Stefano Cresci, Giovanni Da San Martino
| Challenge: | Political campaigns often use coordinated behaviour to identify communities of users who exhibit similar patterns. |
| Approach: | They analysed messages users were exposed to during the UK 2019 election and compared those received by users who shifted communities with others covering the same topics. |
| Outcome: | The results show that political campaigns often use coordinated behaviour to identify communities of users who exhibit similar patterns. |
Does Social Pressure Drive Persuasion in Online Fora? (2021.emnlp-main)
Copied to clipboard
| Challenge: | Using social features, we hypothesize that comments from the ambient community can either affirm the original view or implicitly exert pressure to change it. |
| Approach: | They propose a structured model to capture the ambient community’s sentiment towards the discussion and its effect on persuasion. |
| Outcome: | The proposed model captures the ambient community’s sentiment towards the discussion and its effect on persuasion. |
Breaking Down the Invisible Wall of Informal Fallacies in Online Discussions (2021.acl-long)
Copied to clipboard
| Challenge: | a number of people engage in unsound argumentation techniques to prove a claim on online platforms . fallacies are weak arguments that seem convincing, but their evidence does not prove or disprove the conclusion . |
| Approach: | They propose to use user comments containing fallacy mentions as noisy labels to classify fallacies . they use the pragma-dialectical theory of argumentation to study the most common fallacias on Reddit . |
| Outcome: | The proposed dataset of fallacies on reddit shows that neural models perform better in conversational context. |