相关论文: Extracting Participation in Collective Action from…
We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of…
Mining social media content for tasks such as detecting personal experiences or events, suffer from lexical sparsity, insufficient training data, and inventive lexicons. To reduce the burden of creating extensive labeled data and improve…
Understanding collective decision making at a large-scale, and elucidating how community organization and community dynamics shape collective behavior are at the heart of social science research. In this work we study the behavior of…
Effective mental health support is crucial for alleviating psychological distress. While large language model (LLM)-based assistants have shown promise in mental health interventions, existing research often defines "effective" support…
Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism, their complex…
Optimism about the Internet's potential to bring the world together has been tempered by concerns about its role in inflaming the 'culture wars'. Via mass selection into like-minded groups, online society may be becoming more fragmented and…
Social media, especially Twitter, is being increasingly used for research with predictive analytics. In social media studies, natural language processing (NLP) techniques are used in conjunction with expert-based, manual and qualitative…
This paper is focused on the computational analysis of collective discourse, a collective behavior seen in non-expert content contributions in online social media. We collect and analyze a wide range of real-world collective discourse…
Online communities develop unique characteristics, establish social norms, and exhibit distinct dynamics among their members. Activity in online communities often results in concrete ``off-line'' actions with a broad societal impact (e.g.,…
Social media enables dynamic user engagement with trending topics, and recent research has explored the potential of large language models (LLMs) for response generation. While some studies investigate LLMs as agents for simulating user…
Data extracted from social media platforms, such as Twitter, are both large in scale and complex in nature, since they contain both unstructured text, as well as structured data, such as time stamps and interactions between users. A key…
In a social system individual actions have the potential to trigger spontaneous collective reactions. The way and extent to which the activity (number of actions$-A$) of an individual causes or is connected to the response (number of…
In recent years, social media changed the way individuals participate in social movements. While activists demonstrate on the street to fight for a public goal, members of specific movements can also act collective online. Thus, different…
This study proposes content and interaction analysis techniques for a large repository created from social media content. Though we have presented our study for a large platform dedicated to discussions around financial topics, the proposed…
Summarization has usually relied on gold standard summaries to train extractive or abstractive models. Social media brings a hurdle to summarization techniques since it requires addressing a multi-document multi-author approach. We address…
Many Web platforms rely on user collaboration to generate high-quality content: Wiki, Q&A communities, etc. Understanding and modeling the different collaborative behaviors is therefore critical. However, collaboration patterns are…
In this paper we show the results of our studies carried out in the framework of the European Project SciCafe2.0 in the area of Participatory Engagement models. We present a methodological approach built on participative engagements models…
Sentiment analysis, an increasingly vital field in both academia and industry, plays a pivotal role in machine learning applications, particularly on social media platforms like Reddit. However, the efficacy of sentiment analysis models is…
During crises, social media serves as a crucial coordination tool, but the vast influx of posts--from "actionable" requests and offers to generic content like emotional support, behavioural guidance, or outdated information--complicates…
Accurately estimating how users respond to moderation interventions is paramount for developing effective and user-centred moderation strategies. However, this requires a clear understanding of which user characteristics are associated with…