{"id":3612,"date":"2024-10-07T08:42:17","date_gmt":"2024-10-07T08:42:17","guid":{"rendered":"https:\/\/datalab.luiss.it\/?post_type=ricerche&p=3612"},"modified":"2024-10-07T08:47:41","modified_gmt":"2024-10-07T08:47:41","slug":"lets-make-fact-checking-engaging-a-computational-social-science-approach-for-the-analysis-and-evaluation-of-fact-checking-discourse-at-scale","status":"publish","type":"ricerche","link":"https:\/\/datalab.luiss.it\/en\/ricerche\/lets-make-fact-checking-engaging-a-computational-social-science-approach-for-the-analysis-and-evaluation-of-fact-checking-discourse-at-scale\/","title":{"rendered":"Let\u2019s make fact-checking engaging: a computational social science approach for the analysis and evaluation of fact-checking discourse at scale"},"content":{"rendered":"\n

To counter disinformation, a plethora of fact-checking organizations have arisen, but the fake news phenomenon is far from being solved. Whilst research in computational social science has widely focused on implementing automatic fact-checking, the analysis of the discourse features which make fact-checking impactful has been un- der-investigated. By combining social network analysis, emotion-based sentiment analysis and topic modeling, our study proposes a new methodology to compare and contrast content and audiences of fact-checks and disin- formation messages, with a particular focus on engagement patterns. We apply our approach to a mixed corpus of Italian fact-checks and Italian disinformation accounts on Twitter. Drawing from the results of the analysis, we formulate a set of recommendations to make fact-checking discourse more engaging and impactful, leveraging rhetorical and content-based features.<\/p>\n\n\n\n

Read the full paper here<\/a><\/h2>\n\n\n\n

Di Elena Musi, Lorenzo Federico, Aydan Azimova e Anika Kalra<\/p>\n","protected":false},"excerpt":{"rendered":"

To counter disinformation, a plethora of fact-checking organizations have arisen, but the fake news phenomenon is far from being solved. Whilst research in computational social science has widely focused on implementing automatic fact-checking, the analysis of the discourse features which make fact-checking impactful has been un- der-investigated. By combining social network analysis, emotion-based sentiment analysis […]<\/p>\n","protected":false},"featured_media":3605,"template":"","categories":[],"yoast_head":"\nLet\u2019s make fact-checking engaging: a computational social science approach for the analysis and evaluation of fact-checking discourse at scale - Luiss Data Lab<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datalab.luiss.it\/en\/ricerche\/lets-make-fact-checking-engaging-a-computational-social-science-approach-for-the-analysis-and-evaluation-of-fact-checking-discourse-at-scale\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Let\u2019s make fact-checking engaging: a computational social science approach for the analysis and evaluation of fact-checking discourse at scale - Luiss Data Lab\" \/>\n<meta property=\"og:description\" content=\"To counter disinformation, a plethora of fact-checking organizations have arisen, but the fake news phenomenon is far from being solved. 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