Stance Detection and Open Research Avenues
- URL: http://arxiv.org/abs/2210.12383v1
- Date: Sat, 22 Oct 2022 08:18:09 GMT
- Title: Stance Detection and Open Research Avenues
- Authors: Dilek K\"u\c{c}\"uk and Fazli Can
- Abstract summary: This tutorial aims to cover the state-of-the-art on stance detection and address open research avenues.
The tutorial will be a useful guide for researchers and practitioners of stance detection, social media analysis, information retrieval, and natural language processing.
- Score: 4.83528915855309
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This tutorial aims to cover the state-of-the-art on stance detection and
address open research avenues for interested researchers and practitioners.
Stance detection is a recent research topic where the stance towards a given
target or target set is determined based on the given content and there are
significant application opportunities of stance detection in various domains.
The tutorial comprises two parts where the first part outlines the fundamental
concepts, problems, approaches, and resources of stance detection, while the
second part covers open research avenues and application areas of stance
detection. The tutorial will be a useful guide for researchers and
practitioners of stance detection, social media analysis, information
retrieval, and natural language processing.
Related papers
- A Survey of Stance Detection on Social Media: New Directions and Perspectives [50.27382951812502]
stance detection has emerged as a crucial subfield within affective computing.
Recent years have seen a surge of research interest in developing effective stance detection methods.
This paper provides a comprehensive survey of stance detection techniques on social media.
arXiv Detail & Related papers (2024-09-24T03:06:25Z) - A Review of Human-Object Interaction Detection [6.1941885271010175]
Human-object interaction (HOI) detection plays a key role in high-level visual understanding.
This paper systematically summarizes and discusses the recent work in image-based HOI detection.
arXiv Detail & Related papers (2024-08-20T08:32:39Z) - Expert exploranation for communicating scientific methods -- A case study in conflict research [7.181426448513601]
We show that exploranation can not only support the communication between researchers and a broad audience, but also between researchers directly.
We developed three versions of an interactive visual story to explain the method to conflict researchers.
The positive and extensive feedback from the evaluation shows that expert exploranation is a promising direction for visual storytelling.
arXiv Detail & Related papers (2024-05-23T09:15:46Z) - Collaborative Knowledge Infusion for Low-resource Stance Detection [83.88515573352795]
Target-related knowledge is often needed to assist stance detection models.
We propose a collaborative knowledge infusion approach for low-resource stance detection tasks.
arXiv Detail & Related papers (2024-03-28T08:32:14Z) - Federated Learning for Generalization, Robustness, Fairness: A Survey
and Benchmark [55.898771405172155]
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties.
We provide a systematic overview of the important and recent developments of research on federated learning.
arXiv Detail & Related papers (2023-11-12T06:32:30Z) - Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey [10.665235711722076]
Oriented object detection is one of the most fundamental and challenging tasks in remote sensing.
Recent years have witnessed remarkable progress in oriented object detection using deep learning techniques.
arXiv Detail & Related papers (2023-02-21T06:31:53Z) - Few-Shot Stance Detection via Target-Aware Prompt Distillation [48.40269795901453]
This paper is inspired by the potential capability of pre-trained language models (PLMs) serving as knowledge bases and few-shot learners.
PLMs can provide essential contextual information for the targets and enable few-shot learning via prompts.
Considering the crucial role of the target in stance detection task, we design target-aware prompts and propose a novel verbalizer.
arXiv Detail & Related papers (2022-06-27T12:04:14Z) - Deep Learning for Anomaly Detection: A Review [150.9270911031327]
This paper surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in three high-level categories and 11 fine-grained categories of the methods.
We review their key intuitions, objective functions, underlying assumptions, advantages and disadvantages, and discuss how they address the aforementioned challenges.
arXiv Detail & Related papers (2020-07-06T02:21:16Z) - Stance Detection on Social Media: State of the Art and Trends [5.584060970507506]
Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal.
This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media.
It presents an exhaustive review of stance detection techniques on social media, including the task definition, different types of targets in stance detection, features set used, and various machine learning approaches applied.
arXiv Detail & Related papers (2020-06-05T19:24:16Z) - Deep Learning for Sensor-based Human Activity Recognition: Overview,
Challenges and Opportunities [52.59080024266596]
We present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition.
We first introduce the multi-modality of the sensory data and provide information for public datasets.
We then propose a new taxonomy to structure the deep methods by challenges.
arXiv Detail & Related papers (2020-01-21T09:55:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.