Corporate entities increasingly embed environmental claims in their digital communication to project sustainability awareness. Detecting such claims is critical for regulatory monitoring, corporate accountability, and mitigation of greenwashing practices. Traditional neural network architectures including large language models however, struggle to capture both the complex linguistic structures and the subtle stylistic cues that characterize environmental assertions. In this work, we propose a novel Graph-Augmented Liquid Neural Network (GLNN) architecture for automatic detection of environmental claims in corporate web content. Our approach first models the syntactic and semantic dependencies of text using a Graph Convolutional Network (GCN), while concurrently encoding stylistic features derived from linguistic markers (e.g., LIWC categories) into vector representations. These representations are concatenated and passed into a Liquid Time-Constant (LTC) Network, which provides dynamic adaptability and low-power efficiency by leveraging continuous-time recurrent dynamics. The integration of GCN-based stylistic encoding with LTC networks enables the model to robustly capture both structural dependencies and temporal signal variations inherent in corporate claims, while remaining energy efficient. Extensive experiments on multiple open datasets demonstrate that our model outperforms baseline neural architectures in both accuracy and computational efficiency, highlighting the potential of graph-augmented liquid networks as a foundation for sustainable AI in sustainability monitoring.

This paper proposes the development of a WCAG-compliant chatbot capable of generating multimodal content to enhance usability for all users. While LLM-based chatbots excel in generating varied responses, they often struggle with ambiguous or incomplete queries, leading to misaligned outputs. We introduce a framework that formulates domain-specific, persona-driven follow-up questions to clarify ambiguities, utilizing knowledge graphs and human feedback. The system refines queries before generating responses by employing a domain-Specific Multilayer Hierarchical Relational Graph (MHRG) to model user intent. Our preliminary evaluations indicate that the Accessibility Bot improves response relevance and quality as compared to existing techniques.

The predominance of biased articles and its consumption by the readers is becoming a considerable issue. Researchers across domains have made efforts to mitigate biases in language. However, due to the subjective nature of the problem, it is not trivial to detect bias embedded in a text. In this paper, we propose a deep linguistically informed multi-task transformer-based model to automatically detect bias in written text. The model is fine-tuned with a domain-specific corpus and further trained for learning the objectives. We evaluate the performance of the proposed model with respect to baseline systems across multiple datasets. We observed that augmenting linguistic features along with contextual embedding improves the performance of the neural network model to automatically detect bias in text.

Persuasive conversation leverages conversational strategies by the persuader to change the attitude or behavior of a persuadee towards achieving a specific goal. It involves understanding the linguistic and cognitive principles underlying the organization of strategic disclosures and appeals employed in human persuasion. One of the main challenges of such conversation is the inability of a persuader to detect the outcome of their conversation during the interaction. Such prior knowledge can help a persuader to change their conversation strategy and pre-empt possible conversation failures. In this paper, we propose a technique that analyses conversations to predict whether the persuader is going to successfully persuade the persuadee. We propose a joint model of latent utterance categorization to predict the success or the failure of a persuasive conversation. This latent categorization allows the model to identify high-level conversational contexts that influence patterns of language in a persuasive conversation. We evaluate the performance of our model on an openly available dataset. Our preliminary results demonstrate that the proposed model outperforms competitive baselines.

Automatically generating or ranking distractors for multiple-choice questions (MCQs) is still a challenging problem. In this work, we have focused towards automatic ranking of distractors for MCQs. Accordingly, we have proposed an semantically aware CNN-BiLSTM model. We evaluate our model with different word level embeddings as input over two different openly available datasets. Experimental results demonstrate our proposed model surpasses the performance of the existing baseline models. Furthermore, we have observed that intelligently incorporating word level semantic information along with context specific word embeddings boost up the predictive performance of distractors, which is a promising direction for further research.

In this paper, we tackle the problem of fake news detection from social media by exploiting the presence of echo chamber communities (communities sharing same beliefs) that exist within the social network of the users. By modeling the echo-chambers as closely-connected communities within the social network, we represent a news article as a 3-mode tensor of the structure -and propose a tensor factorization based method to encode the news article in a latent embedding space preserving the community structure. We also propose an extension of the above method, which jointly models the community and content information of the news article through a coupled matrix-tensor factorization framework. We empirically demonstrate the efficacy of our method for the task of Fake News Detection over two real-world datasets.

Today electronic communications have become the prime medium for people to express their opinions and influence the policy preferences. One such popular channel reflecting the voice of the masses is electronic petitions. To understand people's perspective on various issues it is important to know what petitions say. However, due to the sheer volume of the petitions it is difficult to process each petition manually. As each petition talks about a different issue, prioritizing on over other is difficult. To alleviate these challenges, we present an end to end system for generating comprehensive and concise summaries from e-petitions. A petition contains multiple aspects, the core problem, evidence(s) in support of the problem and potential solutions. Therefore, it is imperative that an useful summary should contain information about all these three aspects explicitly. To achieve this, our system generates three aspect based summaries for each petition for better understanding. We also introduce a new annotated petition dataset, developed through crowd-sourcing, that served as gold standard. Our model is tested through quantitative and qualitative evaluations.

In an increasingly digital urban setting, connected & concerned Citizens typically voice their opinions on various civic topics via social media. Efficient and scalable analysis of these citizen voices on social media to derive actionable insights is essential to the development of smart cities. The very nature of the data: heterogeneity and dynamism, the scarcity of gold standard annotated corpora, and the need for multi-dimensional analysis across space, time and semantics, makes urban social media analytics challenging. This workshop is dedicated to the theme of social media analytics for smart cities, with the aim of focusing the interest of CIKM research community on the challenges in mining social media data for urban informatics. The workshop hopes to foster collaboration between researchers working in information retrieval, social media analytics, linguistics; social scientists, and civic authorities, to develop scalable and practical systems for capturing and acting upon real world issues of cities as voiced by their citizens in social media. The aim of this workshop is to encourage researchers to develop techniques for urban analytics of social media data, with specific focus on applying these techniques to practical urban informatics applications for smart cities.