Capturing stance dynamics in social media: open challenges and research directions

Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest and impact. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts as supporting, opposing or being neutral towards the issue at hand. While most prior work in stance detection has investigated datasets that cover short periods of time, interest in investigating longitudinal datasets has recently increased. Evolving dynamics in linguistic and behavioural patterns observed in new data require adapting stance detection systems to deal with the changes. In this survey paper, we investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media. We perform a critical review of emerging research considering dynamics, exploring different semantic and pragmatic factors that impact linguistic data in general, and stance in particular. We further discuss current directions in capturing stance dynamics in social media. We discuss the challenges encountered when dealing with stance dynamics, identify open challenges and discuss future directions in three key dimensions: utterance, context and influence

QMUL-SDS @ SardiStance2020: Leveraging network interactions to boost performance on stance detection using knowledge graphs

This paper presents our submission to the SardiStance 2020 shared task, describing the architecture used for Task A and Task B. While our submission for Task A did not exceed the baseline, retraining our model using all the training tweets, showed promising results leading to (f-avg 0.601) using bidirectional LSTM with BERT multilingual embedding for Task A. For our submission for Task B, we ranked 6th (f-avg 0.709). With further investigation, our best experimented settings increased performance from (f-avg 0.573) to (f-avg 0.733) with same architecture and parameter settings and after only incorporating social interaction features- highlighting the impact of social interaction on the model's performance

Adapting to Change: The Temporal Persistence of Text Classifiers in the Context of Longitudinally Evolving Data

This thesis delves into the evolving landscape of NLP, particularly focusing on the temporal persistence of text classifiers amid the dynamic nature of language use. The primary objective is to understand how changes in language patterns over time impact the performance of text classification models and to develop methodologies for maintaining their effectiveness. The research begins by establishing a theoretical foundation for text classification and temporal data analysis, highlighting the challenges posed by the evolving use of language and its implications for NLP models. A detailed exploration of various datasets, including the stance detection and sentiment analysis datasets, sets the stage for examining these dynamics. The characteristics of the datasets, such as linguistic variations and temporal vocabulary growth, are carefully examined to understand their influence on the performance of the text classifier. A series of experiments are conducted to evaluate the performance of text classifiers across different temporal scenarios. The findings reveal a general trend of performance degradation over time, emphasizing the need for classifiers that can adapt to linguistic changes. The experiments assess models' ability to estimate past and future performance based on their current efficacy and linguistic dataset characteristics, leading to valuable insights into the factors influencing model longevity. Innovative solutions are proposed to address the observed performance decline and adapt to temporal changes in language use over time. These include incorporating temporal information into word embeddings and comparing various methods across temporal gaps. The Incremental Temporal Alignment (ITA) method emerges as a significant contributor to enhancing classifier performance in same-period experiments, although it faces challenges in maintaining effectiveness over longer temporal gaps. Furthermore, the exploration of machine learning and statistical methods highlights their potential to maintain classifier accuracy in the face of longitudinally evolving data. The thesis culminates in a shared task evaluation, where participant-submitted models are compared against baseline models to assess their classifiers' temporal persistence. This comparison provides a comprehensive understanding of the short-term, long-term, and overall persistence of their models, providing valuable information to the field. The research identifies several future directions, including interdisciplinary approaches that integrate linguistics and sociology, tracking textual shifts on online platforms, extending the analysis to other classification tasks, and investigating the ethical implications of evolving language in NLP applications. This thesis contributes to the NLP field by highlighting the importance of evaluating text classifiers' temporal persistence and offering methodologies to enhance their sustainability in dynamically evolving language environments. The findings and proposed approaches pave the way for future research, aiming at the development of more robust, reliable, and temporally persistent text classification models

QMUL-SDS @ DIACR-Ita: Evaluating unsupervised diachronic lexical semantics classification in Italian

In this paper, we present the results and main findings of our system for the DIACR-Ita 2020 Task. Our system focuses on using variations of training sets and different semantic detection methods. The task involves training, aligning and predicting a word's vector change from two diachronic Italian corpora. We demonstrate that using Temporal Word Embeddings with a Compass C-BOW model is more effective compared to different approaches including Logistic Regression and a Feed Forward Neural Network using accuracy. Our model ranked 3rd with an accuracy of 83.3%

Strategy for the management of diabetic macular edema: the European Vitreo-Retinal Society macular edema study

Objective. To compare the efficacy of different therapies in the treatment of diabetic macular edema (DME). Design. Nonrandomized, multicenter clinical study. Participants. 86 retina specialists from 29 countries provided clinical information on 2,603 patients with macular edema including 870 patients with DME. Methods. Reported data included the type and number of treatment(s) performed, the pre-and posttreatment visual acuities, and other clinical findings.The results were analyzed by the French INSEE (National Institute of Statistics and Economic Studies). Main Outcome Measures. Mean change of visual acuity and mean number of treatments performed. Results.The change in visual acuity over time in response to each treatment was plotted in second order polynomial regression trend lines. Intravitreal triamcinolone monotherapy resulted in some improvement in vision. Treatmentwith threshold or subthreshold grid laser also resulted in minimal vision gain. Anti-VEGF therapy resulted in more significant visual improvement. Treatment with pars plana vitrectomy and internal limiting membrane (ILM) peeling alone resulted in an improvement in vision greater than that observed with anti-VEGF injection alone. In our DME study, treatment with vitrectomy and ILM peeling alone resulted in the better visual improvement compared to other therapies

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries