Stance detection enables the inference of attitudes from human communications.Automatic stance identification was mostly cast as a classification problem.However, stance decisions involve complex judgments, which can be nowadays generated by prompting Large Language Models (LLMs).In this paper we present a new method for stance identification which (1) relies on a new prompting framework, called Tree-of-Counterfactual prompting; (2) operates not only on textual communications, but also on images; (3) allows more than one stance object type; and (4) requires no examples of stance attribution, thus it is a "Tabula Rasa" Zero-Shot Stance Detection (TR-ZSSD) method.Our experiments indicate surprisingly promising results, outperforming fine-tuned stance detection systems.

Stance as an expression of an author’s standpoint and as a means of communication has long been studied by computational linguists. Automatically identifying the stance of a subject toward an object is an active area of research in natural language processing. Significant work has employed topics and claims as the object of stance, with frames of communication becoming more recently considered as alternative objects of stance. However, little attention has been paid to finding what are the benefits and what are the drawbacks when inferring the stance of a text towards different possible stance objects. In this paper we seek to answer this question by analyzing the implied knowledge and the judgments required when deciding the stance of a text towards each stance object type. Our analysis informed experiments with models capable of inferring the stance of a text towards any of the stance object types considered, namely topics, claims, and frames of communication. Experiments clearly indicate that it is best to infer the stance of a text towards a frame of communication, rather than a claim or a topic. It is also better to infer the stance of a text towards a claim rather than a topic. Therefore we advocate that rather than continuing efforts to annotate the stance of texts towards topics, it is better to use those efforts to produce annotations towards frames of communication. These efforts will allow us to better capture the stance towards claims and topics as well.

Frames of Communication (FoCs) are ubiquitous in social media discourse.They define what counts as a problem, diagnose what is causing the problem, elicit moral judgments and imply remedies for resolving the problem (Entman, 1993).Most research on automatic frame detection involved the recognition of the problems addressed by frames, but did not consider the articulation of frames.Articulating an FoC involves reasoning with salient problems, their cause and eventual solution.In this paper we present a method for Discovering and Articulating FoCs (DA-FoC) that relies on a combination of Chain-of-Thought prompting (Wei et al., 2022a) of large language models (LLMs) with In-Context Active Curriculum Learning.Very promising evaluation results indicate that 86.72% of the FoCs encoded by communication experts on the same reference dataset were also uncovered by DA-FoC.Moreover, DA-FoC uncovered many new FoCs, which escaped the experts.Interestingly, 55.1% of the known FoCs were judged as being better articulated than the human-written ones, while 93.8% of the new FoCs were judged as having sound rationale and being clearly articulated.

Frames of communication are often evoked in multimedia documents.When an author decides to add an image to a text, one or both of the modalities may evoke a communication frame.Moreover, when evoking the frame, the author also conveys her/his stance towards the frame.Until now, determining if the author is in favor of, against or has no stance towards the frame was performed automatically only when processing texts.This is due to the absence of stance annotations on multimedia documents.In this paper we introduce MMVAX-STANCE, a dataset of 11,300 multimedia documents retrieved from social media, which have stance annotations towards 113 different frames of communication.This dataset allowed us to experiment with several models of multimedia stance detection, which revealed important interactions between texts and images in the inference of stance towards communication frames.When inferring the text/image relations, a set of 46,606 synthetic examples of multimodal documents with known stance was generated.This greatly impacted the quality of identifying multimedia stance, yielding an improvement of 20% in F1-score. Component Definition Examples of Frames of Communication ConfidenceTrust in the security and effectiveness of 2 Pfizer COVID-19 vaccine may cause anaphylaxis vaccinations, the health authorities, and in people with polyethylene glycol (PEG) allergy.the health officials who recommend 2 The Government has provided plenty of safety and develop vaccines.information about the COVID-19 vaccines. ComplacencyComplacency and laziness to get vaccinated 2 Preference for getting COVID-19 and fighting due to low perceived risk of infections.it off than vaccinating. ConstraintsStructural or psychological hurdles that 2 It takes courage both to vaccinate against make vaccination difficult or costly. COVID-19 and to refuse the vaccine. CalculationDegree to which personal costs and benefits 2 COVID-19 vaccines protect against the emerging of vaccination are weighted. variants. CollectiveWillingness to protect others and to 2 Vaccination is key in protecting yourself and others Responsibility eliminate infectious diseases.against COVID-19.Compliance Support for societal monitoring and sanctioning 2 People choosing not to get the COVID-19 vaccine of people who are not vaccinated.should not lose venue access/travel to some countries. ConspiracyConspiracy thinking and belief in 2 COVID-19 vaccines make you 5G compatible.fake news related to vaccination.2 The COVID vaccine renders pregnancies risky.

OBJECTIVE: The rapidly growing body of communications during the COVID-19 pandemic posed a challenge to information seekers, who struggled to find answers to their specific and changing information needs. We designed a Question Answering (QA) system capable of answering ad-hoc questions about the COVID-19 disease, its causal virus SARS-CoV-2, and the recommended response to the pandemic. MATERIALS AND METHODS: The QA system incorporates, in addition to relevance models, automatic generation of questions from relevant sentences. We relied on entailment between questions for (1) pinpointing answers and (2) selecting novel answers early in the list of its results. RESULTS: The QA system produced state-of-the-art results when processing questions asked by experts (eg, researchers, scientists, or clinicians) and competitive results when processing questions asked by consumers of health information. Although state-of-the-art models for question generation and question entailment were used, more than half of the answers were missed, due to the limitations of the relevance models employed. DISCUSSION: Although question entailment enabled by automatic question generation is the cornerstone of our QA system's architecture, question entailment did not prove to always be reliable or sufficient in ranking the answers. Question entailment should be enhanced with additional inferential capabilities. CONCLUSION: The QA system presented in this article produced state-of-the-art results processing expert questions and competitive results processing consumer questions. Improvements should be considered by using better relevance models and enhanced inference methods. Moreover, experts and consumers have different answer expectations, which should be accounted for in future QA development.

Billions of COVID-19 vaccines have been administered, but many remain hesitant. Misinformation about the COVID-19 vaccines and other vaccines, propagating on social media, is believed to drive hesitancy towards vaccination. The ability to automatically recognize misinformation targeting vaccines on Twitter depends on the availability of data resources. In this paper we present VaccineLies, a large collection of tweets propagating misinformation about two vaccines: the COVID-19 vaccines and the Human Papillomavirus (HPV) vaccines. Misinformation targets are organized in vaccine-specific taxonomies, which reveal the misinformation themes and concerns. The ontological commitments of the misinformation taxonomies provide an understanding of which misinformation themes and concerns dominate the discourse about the two vaccines covered in VaccineLies. The organization into training, testing and development sets of VaccineLies invites the development of novel supervised methods for detecting misinformation on Twitter and identifying the stance towards it. Furthermore, VaccineLies can be a stepping stone for the development of datasets focusing on misinformation targeting additional vaccines.

Although billions of COVID-19 vaccines have been administered, too many people remain hesitant. Misinformation about the COVID-19 vaccines, propagating on social media, is believed to drive hesitancy towards vaccination. However, exposure to misinformation does not necessarily indicate misinformation adoption. In this paper we describe a novel framework for identifying the stance towards misinformation, relying on attitude consistency and its properties. The interactions between attitude consistency, adoption or rejection of misinformation and the content of microblogs are exploited in a novel neural architecture, where the stance towards misinformation is organized in a knowledge graph. This new neural framework is enabling the identification of stance towards misinformation about COVID-19 vaccines with state-of-the-art results. The experiments are performed on a new dataset of misinformation towards COVID-19 vaccines, called CoVaxLies, collected from recent Twitter discourse. Because CoVaxLies provides a taxonomy of the misinformation about COVID-19 vaccines, we are able to show which type of misinformation is mostly adopted and which is mostly rejected.

OBJECTIVE: We explored how knowledge embeddings (KEs) learned from the Unified Medical Language System (UMLS) Metathesaurus impact the quality of relation extraction on 2 diverse sets of biomedical texts. MATERIALS AND METHODS: Two forms of KEs were learned for concepts and relation types from the UMLS Metathesaurus, namely lexicalized knowledge embeddings (LKEs) and unlexicalized KEs. A knowledge embedding encoder (KEE) enabled learning either LKEs or unlexicalized KEs as well as neural models capable of producing LKEs for mentions of biomedical concepts in texts and relation types that are not encoded in the UMLS Metathesaurus. This allowed us to design the relation extraction with knowledge embeddings (REKE) system, which incorporates either LKEs or unlexicalized KEs produced for relation types of interest and their arguments. RESULTS: The incorporation of either LKEs or unlexicalized KE in REKE advances the state of the art in relation extraction on 2 relation extraction datasets: the 2010 i2b2/VA dataset and the 2013 Drug-Drug Interaction Extraction Challenge corpus. Moreover, the impact of LKEs is superior, achieving F1 scores of 78.2 and 82.0, respectively. DISCUSSION: REKE not only highlights the importance of incorporating knowledge encoded in the UMLS Metathesaurus in a novel way, through 2 possible forms of KEs, but it also showcases the subtleties of incorporating KEs in relation extraction systems. CONCLUSIONS: Incorporating LKEs informed by the UMLS Metathesaurus in a relation extraction system operating on biomedical texts shows significant promise. We present the REKE system, which establishes new state-of-the-art results for relation extraction on 2 datasets when using LKEs.