Contextual meme understanding decodes implicit meaning from ambiguous visual metaphors and social context, which is crucial for online communication analysis. However, existing linear reasoning paradigms reductively cast this process as deterministic decoding, overlooking the fundamental reality that meme interpretation necessitates the dynamic alignment of multimodal cues. Lacking mechanisms to measure and correct misalignment, these static models inevitably allow initial perceptual failures to cascade into irreversible hallucinations. To address this, we propose Semantic Energy Entropy Descent (SEED), a framework that reformulates contextual meme understanding as an energy minimization problem within a discrete semantic space. Specifically, SEED constructs a Convergent Heterogeneous Thought Tree (CHTT) as the optimization workspace, where an Evaluator Agent quantifies the semantic inconsistency of hypotheses via a Semantic Energy Mechanism. By calculating a Discrete Semantic Gradient as structured feedback, the framework activates a Supplier Agent to retrieve knowledge and context evidence and a Reasoner Agent to refine explanations, thereby orchestrating Semantic Descent Dynamics that iteratively drive the reasoning trajectory to converge on a stable, low-entropy interpretation. Experimental results show that SEED consistently outperforms strong baselines on both classification and generation, reducing logical hallucinations and improving cultural grounding.

Query performance prediction (QPP) is a critical task in information retrieval. It estimates retrieval quality for a given query without relying on relevance judgments. While recent approaches have leveraged pretrained (large) language models with binary- or cross-encoder architectures, they struggle to capture subtle semantic differences (nuances that make similar sentences mean different things) between queries and documents in QPP, limiting prediction accuracy. To address this issue, we present Angle-QPP, a novel and efficient binary-encoder QPP approach with three key innovations: (1) the use of Large Language Models (LLMs) of varying scales to learn rich contextual semantics, (2) a contrastive learning warm-up phase to obtain high-quality initial representation quality, and (3) an angle-based interaction mechanism operating in complex embedding space to effectively capture subtle semantic relationships between queries and documents. Comprehensive experiments on TREC DL 2019, 2020, 2021, and 2022 datasets demonstrate that the proposed Angle-QPP significantly outperforms existing methods across all evaluation metrics. Notably, Angle-QPP models with 0.5B, 1.5B, and 3B parameters achieve \(6.4\%\) , \(11.2\%\) , and \(13.9\%\) absolute improvements in prediction accuracy over the previous state-of-the-art binary-encoder BERT-QPP, respectively. It demonstrates the scalability and effectiveness of the proposed method. Ablation studies confirm the effectiveness of both the angle interaction mechanism and contrastive learning warm-up components. Our analysis further reveals that scaling up LLM size consistently improves QPP performance, providing valuable insights for the design of future QPP systems.

With the increasing popularity of social media, online interpersonal communication now plays an essential role in people’s everyday information exchange. Whether and how a newcomer can better engage in the community has attracted great interest due to its application in many scenarios. Although some prior works that explore early socialization have obtained salient achievements, they are focusing on sociological surveys based on the small group. To help individuals get through the early socialization period and engage well in online conversations, we study a novel task to foresee whether a newcomer’s message will be responded to by other participants in a multi-party conversation (henceforth Successful New-entry Prediction)1. The task would be an important part of the research in online assistants and social media. To further investigate the key factors indicating such engagement success, we employ an unsupervised neural network, Variational Auto-Encoder (VAE), to examine the topic content and discourse behavior from newcomer’s chatting history and conversation’s ongoing context. Furthermore, two large-scale datasets, from Reddit and Twitter, are collected to support further research on new-entries. Extensive experiments on both Twitter and Reddit datasets show that our model significantly outperforms all the baselines and popular neural models. Additional explainable and visual analyses on new-entry behavior shed light on how to better join in others’ discussions.

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.

In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the increasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion. Extensive experiments have been conducted on argumentative conversations on both social media and supreme court. The results show that our model outperforms state-of-the-art models in identifying persuasive arguments via explicitly exploring dynamic factors of topic and discourse. We further analyze the effects of topics and discourse on persuasiveness, and find that they are both useful — topics provide concrete evidence while superior discourse styles may bias participants, especially in social media arguments. In addition, we draw some findings from our empirical results, which will help people better engage in future persuasive conversations.

Millions of messages are produced on microblog platforms every day, leading to the pressing need for automatic identification of key points from the massive texts. To absorb salient content from the vast bulk of microblog posts, this article focuses on the task of microblog keyphrase extraction. In previous work, most efforts treat messages as independent documents and might suffer from the data sparsity problem exhibited in short and informal microblog posts. On the contrary, we propose to enrich contexts via exploiting conversations initialized by target posts and formed by their replies, which are generally centered around relevant topics to the target posts and therefore helpful for keyphrase identification. Concretely, we present a neural keyphrase extraction framework, which has 2 modules: a conversation context encoder and a keyphrase tagger. The conversation context encoder captures indicative representation from their conversation contexts and feeds the representation into the keyphrase tagger, and the keyphrase tagger extracts salient words from target posts. The 2 modules were trained jointly to optimize the conversation context encoding and keyphrase extraction processes. In the conversation context encoder, we leverage hierarchical structures to capture the word‐level indicative representation and message‐level indicative representation hierarchically. In both of the modules, we apply character‐level representations, which enables the model to explore morphological features and deal with the out‐of‐vocabulary problem caused by the informal language style of microblog messages. Extensive comparison results on real‐life data sets indicate that our model outperforms state‐of‐the‐art models from previous studies.