Modeling Global and Local Interactions for Online Conversation Recommendation

The popularity of social media platforms results in a huge volume of online conversations produced every day. To help users better engage in online conversations, this article presents a novel framework to automatically recommend conversations to users based on what they said and how they behaved in their chatting histories. While prior work mostly focuses on post-level recommendation, we aim to explore conversation context and model the interaction patterns therein. Furthermore, to characterize personal interests from interleaving user interactions, we learn (1) global interactions , represented by topic and discourse word clusters to reflect users’ content and pragmatic preferences, and (2) local interactions , encoding replying relations and chronological order of conversation turns to characterize users’ prior behavior. Built on collaborative filtering, our model captures global interactions via discovering word distributions to represent users’ topical interests and discourse behaviors, while local interactions are explored with graph-structured networks exploiting both reply structure and temporal features. Extensive experiments on three datasets from Twitter and Reddit show that our model coupling global and local interactions significantly outperforms the state-of-the-art model. Further analyses show that our model is able to capture meaningful features from global and local interactions, which results in its superior performance in conversation recommendation.

Lesbonormatiivisuuksien ristipaineessa: määrällistä ja laadullista analyysiä Suomi24-verkkokeskusteluista

Artikkelissamme identifioimme tilastollisella avainsana-analyysillä Suomi24-keskustelufoorumilla tuotettuja lesboerityisiä diskursseja, joissa – toisin kuin homodiskursseissa – keskitytään seksuaaliseen suuntautumiseen, sukupuoleen ja ulkonäköön. Lesbodiskursseja tarkastelemme edelleen teoriavetoisen kriittisen lähiluvun avulla. Analyysi nostaa esiin keskusteluja, joissa heteronormatiivisuuden kautta määrittyvä lesbous voidaan kokea hyvinkin ristiriitaiseksi ja ahdistavaksi. Käyttämällä hyväksi lesbomatriisin ja lesbonormatiivisuuden käsitteitä osoitamme, kuinka sekä nais- että miesfeminiinistä lesboutta ja lesboparisuhteita koskevissa keskusteluissa nojataan yhtäältä normatiiviseen ajattelutapaan ja toisaalta haastetaan sitä.Avainsanat: lesbous, lesbonormatiivisuus, keskustelufoorumit, korpusavusteinen diskurssintutkimusCross pressured by lesbonormativity: Quantitative and qualitative analysis of online conversations on the Suomi24 discussion forumIn our article, we use statistical keyword analysis to identify typical lesbian discourses at the Suomi24 discussion forum. Contrary to gay discourses, they focus on sexual orientation, gender and looks. These lesbian discourses are further analyzed by critical close reading. The analysis highlights conversations in which lesbianism can feel extremely conflicting and distressing when defined in heteronormative terms. Moreover, by using the concepts of lesbian matrix and lesbonormativity, we show how discussions concerning both feminine and masculine appearing lesbians as well as lesbian relationships rely on normative thinking, while also challenging it.Keywords: lesbianism, lesbonormativity, discussion forums, corpus-assisted discourse studies

Infodemic Signal Detection During the COVID-19 Pandemic: Development of a Methodology for Identifying Potential Information Voids in Online Conversations

Background The COVID-19 pandemic has been accompanied by an infodemic: excess information, including false or misleading information, in digital and physical environments during an acute public health event. This infodemic is leading to confusion and risk-taking behaviors that can be harmful to health, as well as to mistrust in health authorities and public health responses. The World Health Organization (WHO) is working to develop tools to provide an evidence-based response to the infodemic, enabling prioritization of health response activities. Objective In this work, we aimed to develop a practical, structured approach to identify narratives in public online conversations on social media platforms where concerns or confusion exist or where narratives are gaining traction, thus providing actionable data to help the WHO prioritize its response efforts to address the COVID-19 infodemic. Methods We developed a taxonomy to filter global public conversations in English and French related to COVID-19 on social media into 5 categories with 35 subcategories. The taxonomy and its implementation were validated for retrieval precision and recall, and they were reviewed and adapted as language about the pandemic in online conversations changed over time. The aggregated data for each subcategory were analyzed on a weekly basis by volume, velocity, and presence of questions to detect signals of information voids with potential for confusion or where mis- or disinformation may thrive. A human analyst reviewed and identified potential information voids and sources of confusion, and quantitative data were used to provide insights on emerging narratives, influencers, and public reactions to COVID-19–related topics. Results A COVID-19 public health social listening taxonomy was developed, validated, and applied to filter relevant content for more focused analysis. A weekly analysis of public online conversations since March 23, 2020, enabled quantification of shifting interests in public health–related topics concerning the pandemic, and the analysis demonstrated recurring voids of verified health information. This approach therefore focuses on the detection of infodemic signals to generate actionable insights to rapidly inform decision-making for a more targeted and adaptive response, including risk communication. Conclusions This approach has been successfully applied to identify and analyze infodemic signals, particularly information voids, to inform the COVID-19 pandemic response. More broadly, the results have demonstrated the importance of ongoing monitoring and analysis of public online conversations, as information voids frequently recur and narratives shift over time. The approach is being piloted in individual countries and WHO regions to generate localized insights and actions; meanwhile, a pilot of an artificial intelligence–based social listening platform is using this taxonomy to aggregate and compare online conversations across 20 countries. Beyond the COVID-19 pandemic, the taxonomy and methodology may be adapted for fast deployment in future public health events, and they could form the basis of a routine social listening program for health preparedness and response planning.