Promoting Reliable Knowledge about Climate Change: A Systematic Review of Effective Measures to Resist Manipulation on Social Media

• URL: http://arxiv.org/abs/2410.23814v1
• Date: Thu, 31 Oct 2024 10:58:38 GMT
• Title: Promoting Reliable Knowledge about Climate Change: A Systematic Review of Effective Measures to Resist Manipulation on Social Media
• Authors: Aliaksandr Herasimenka, Xianlingchen Wang, Ralph Schroeder,
• Abstract summary: We find that commonly recommended approaches to addressing manipulation about climate change include corrective information sharing and education campaigns targeting media literacy. We locate research gaps that include the lack of attention to large commercial and political entities involved in generating and disseminating manipulation. Evidence drawn from many studies demonstrates an emerging consensus about policies required to promote reliable knowledge about climate change and resist manipulation.
• Score: 11.476777375043381
• License:
• Abstract: We present a systematic review of peer-reviewed research into ways to mitigate manipulative information about climate change on social media. Such information may include disinformation, harmful influence campaigns, or the unintentional spread of misleading information. We find that commonly recommended approaches to addressing manipulation about climate change include corrective information sharing and education campaigns targeting media literacy. However, most relevant research fails to test the approaches and interventions it proposes. We locate research gaps that include the lack of attention to large commercial and political entities involved in generating and disseminating manipulation, video- and image-focused platforms, and computational methods to collect and analyze data. Evidence drawn from many studies demonstrates an emerging consensus about policies required to promote reliable knowledge about climate change and resist manipulation.

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)
• PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent [71.20471076045916]
  Propaganda plays a critical role in shaping public opinion and fueling disinformation. Propainsight systematically dissects propaganda into techniques, arousal appeals, and underlying intent. Propagaze combines human-annotated data with high-quality synthetic data.
  arXiv  Detail & Related papers  (2024-09-19T06:28:18Z)
• Indexing and Visualization of Climate Change Narratives Using BERT and Causal Extraction [2.7325857919669327]
  We use two natural language processing methods, BERT (Bidirectional Representations from Transformers) and causal extraction, to analyze newspaper articles on climate change. The novelty of the methodology could extract and quantify the causal relationships assumed by the newspaper's writers.
  arXiv  Detail & Related papers  (2024-08-03T11:05:41Z)
• Unlearning Climate Misinformation in Large Language Models [17.95497650321137]
  Misinformation regarding climate change is a key roadblock in addressing one of the most serious threats to humanity. This paper investigates factual accuracy in large language models (LLMs) regarding climate information.
  arXiv  Detail & Related papers  (2024-05-29T23:11:53Z)
• ClimateNLP: Analyzing Public Sentiment Towards Climate Change Using Natural Language Processing [0.0]
  This paper employs natural language processing (NLP) techniques to analyze climate change discourse and quantify the sentiment of climate change-related tweets. The objective is to discern the sentiment individuals express and uncover patterns in public opinion concerning climate change.
  arXiv  Detail & Related papers  (2023-10-12T07:48:50Z)
• Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection [115.83992775004043]
  Recent advances in deep learning, particularly deep generative models, open the doors for producing perceptually convincing images and videos at a low cost. This paper provides a comprehensive review of the current media tampering detection approaches, and discusses the challenges and trends in this field for future research.
  arXiv  Detail & Related papers  (2022-12-12T02:54:08Z)
• Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes [79.79659145328856]
  We argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation. We make the case that polarization and misinformation adherence are closely tied.
  arXiv  Detail & Related papers  (2022-06-30T12:34:24Z)
• Machine learning reveals how personalized climate communication can both succeed and backfire [55.41644538483948]
  We show that online advertisements increase some people's belief in climate change while resulting in decreased belief in others. In particular, we show that the effect of the advertisements could change depending on people's age and ethnicity.
  arXiv  Detail & Related papers  (2021-09-10T20:47:34Z)
• NLP for Climate Policy: Creating a Knowledge Platform for Holistic and Effective Climate Action [2.482368922343792]
  The paper thematically discusses how NLP techniques could be employed in climate policy research. We exemplify symbiosis of NLP and Climate Policy Research via four methodologies. The present theme paper further argues that creating a knowledge platform would help in the formulation of a holistic climate policy.
  arXiv  Detail & Related papers  (2021-05-12T12:30:02Z)
• Cross-Domain Learning for Classifying Propaganda in Online Contents [67.10699378370752]
  We present an approach to leverage cross-domain learning, based on labeled documents and sentences from news and tweets, as well as political speeches with a clear difference in their degrees of being propagandistic. Our experiments demonstrate the usefulness of this approach, and identify difficulties and limitations in various configurations of sources and targets for the transfer step.
  arXiv  Detail & Related papers  (2020-11-13T10:19:13Z)
• From Talk to Action with Accountability: Monitoring the Public Discussion of Policy Makers with Deep Neural Networks and Topic Modelling [0.0]
  We propose a multi-source topic aggregation system (MuSTAS) MuSTAS processes policy makers speech and rhetoric from several publicly available sources into an easily digestible topic summary. This topic digest will serve the general public and civil society in assessing where, how, and when politicians talk about climate and climate policies.
  arXiv  Detail & Related papers  (2020-10-16T12:21:01Z)

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.