A survey on extremism analysis using Natural Language Processing

• URL: http://arxiv.org/abs/2104.04069v2
• Date: Wed, 21 Apr 2021 16:40:20 GMT
• Title: A survey on extremism analysis using Natural Language Processing
• Authors: Javier Torregrosa, Gema Bello-Orgaz, Eugenio Martinez-Camara, Javier Del Ser, David Camacho
• Abstract summary: This survey aims to review the contributions of NLP to the field of extremism research. The content includes a description and comparison of the frequently used NLP techniques, how they were applied and the insights they provided. future trends, challenges and directions derived from these highlights are suggested.
• Score: 7.885207996427683
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extremism research has grown as an open problem for several countries during recent years, especially due to the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. Natural Language Processing (NLP) represents a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by this groups, with the final objective of detecting and preventing its spread. This survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. The content includes a description and comparison of the frequently used NLP techniques, how they were applied, the insights they provided, the most frequently used NLP software tools and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested.

Related papers

• The Nature of NLP: Analyzing Contributions in NLP Papers [77.31665252336157]
  We quantitatively investigate what constitutes NLP research by examining research papers. Our findings reveal a rising involvement of machine learning in NLP since the early nineties. In post-2020, there has been a resurgence of focus on language and people.
  arXiv  Detail & Related papers  (2024-09-29T01:29:28Z)
• A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions [0.0]
  Large Language Models (LLMs) have revolutionized various applications in natural language processing (NLP) by providing unprecedented text generation, translation, and comprehension capabilities. Their widespread deployment has brought to light significant concerns regarding biases embedded within these models. This paper presents a comprehensive survey of biases in LLMs, aiming to provide an extensive review of the types, sources, impacts, and mitigation strategies related to these biases.
  arXiv  Detail & Related papers  (2024-09-24T19:50:38Z)
• On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs [20.589396689900614]
  This paper addresses three fundamental questions: Why do we need interpretability, what are we interpreting, and how? By exploring these questions, we examine existing interpretability paradigms, their properties, and their relevance to different stakeholders. Our analysis reveals significant disparities between NLP developers and non-developer users, as well as between research fields, underscoring the diverse needs of stakeholders.
  arXiv  Detail & Related papers  (2024-07-27T08:00:27Z)
• Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models [52.24001776263608]
  This comprehensive survey delves into the recent strides in HS moderation. We highlight the burgeoning role of large language models (LLMs) and large multimodal models (LMMs) We identify existing gaps in research, particularly in the context of underrepresented languages and cultures.
  arXiv  Detail & Related papers  (2024-01-30T03:51:44Z)
• Natural Language Processing for Dialects of a Language: A Survey [56.93337350526933]
  State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets. This survey delves into an important attribute of these datasets: the dialect of a language. Motivated by the performance degradation of NLP models for dialectic datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
  arXiv  Detail & Related papers  (2024-01-11T03:04:38Z)
• Key-phrase boosted unsupervised summary generation for FinTech organization [4.583461218488076]
  Some of the NLP applications such as intent detection, sentiment classification, text summarization can help FinTech organizations to utilize the social media language data. We design an unsupervised phrase-based summary generation from social media data, using 'Action-Object' pairs (intent phrases) We evaluate the proposed method with other key-phrase based summary generation methods in the direction of contextual information of various Reddit discussion threads.
  arXiv  Detail & Related papers  (2023-10-16T11:30:47Z)
• Exploring the Landscape of Natural Language Processing Research [3.3916160303055567]
  Several NLP-related approaches have been surveyed in the research community. A comprehensive study that categorizes established topics, identifies trends, and outlines areas for future research remains absent. As a result, we present a structured overview of the research landscape, provide a taxonomy of fields of study in NLP, analyze recent developments in NLP, summarize our findings, and highlight directions for future work.
  arXiv  Detail & Related papers  (2023-07-20T07:33:30Z)
• Sociodemographic Bias in Language Models: A Survey and Forward Path [7.337228289111424]
  Sociodemographic bias in language models (LMs) has the potential for harm when deployed in real-world settings. This paper presents a comprehensive survey of the past decade of research on sociodemographic bias in LMs.
  arXiv  Detail & Related papers  (2023-06-13T22:07:54Z)
• A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why? [84.46288849132634]
  We propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques. We define three variables to encompass diverse facets of the evolution of research topics within NLP. We utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data.
  arXiv  Detail & Related papers  (2023-05-22T11:08:00Z)
• A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs [77.34726150561087]
  We survey the current research landscape on word, sentence and knowledge graph embedding algorithms. We provide a classification of the relevant alignment techniques and discuss benchmark datasets used in this field of research.
  arXiv  Detail & Related papers  (2020-10-26T16:08:13Z)
• Sentiment Analysis Based on Deep Learning: A Comparative Study [69.09570726777817]
  The study of public opinion can provide us with valuable information. The efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems.
  arXiv  Detail & Related papers  (2020-06-05T16:28:10Z)

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.