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Extracting attributed verification and debunking reports from social media: MediaEval-2015 trust and credibility analysis of image and video

Extracting attributed verification and debunking reports from social media: MediaEval-2015 trust and credibility analysis of image and video
Extracting attributed verification and debunking reports from social media: MediaEval-2015 trust and credibility analysis of image and video
Journalists are increasingly turning to technology for pre-filtering and automation of the simpler parts of the verification process. We present results from our semi-automated approach to trust and credibility analysis of tweets referencing suspicious images and videos. We use natural language processing to extract evidence from tweets in the form of fake & genuine claims attributed to trusted and untrusted sources. Results for team UoS-ITI in the
MediaEval 2015 Verifying Multimedia Use task are reported. Our 'fake' tweet classifier precision scores range from 0.94 to 1.0 (recall 0.43 to 0.72), and our 'real' tweet classifier precision scores range from 0.74 to 0.78 (recall 0.51 to 0.74). Image classification precision scores range from 0.62 to 1.0 (recall 0.04 to 0.23). Our approach can automatically alert journalists in real-time to trustworthy claims verifying or debunking viral images or videos
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f

Middleton, Stuart (2015) Extracting attributed verification and debunking reports from social media: MediaEval-2015 trust and credibility analysis of image and video. MediaEval 2015, Wurzen, Germany. 13 - 14 Sep 2015. 3 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Journalists are increasingly turning to technology for pre-filtering and automation of the simpler parts of the verification process. We present results from our semi-automated approach to trust and credibility analysis of tweets referencing suspicious images and videos. We use natural language processing to extract evidence from tweets in the form of fake & genuine claims attributed to trusted and untrusted sources. Results for team UoS-ITI in the
MediaEval 2015 Verifying Multimedia Use task are reported. Our 'fake' tweet classifier precision scores range from 0.94 to 1.0 (recall 0.43 to 0.72), and our 'real' tweet classifier precision scores range from 0.74 to 0.78 (recall 0.51 to 0.74). Image classification precision scores range from 0.62 to 1.0 (recall 0.04 to 0.23). Our approach can automatically alert journalists in real-time to trustworthy claims verifying or debunking viral images or videos

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More information

Published date: September 2015
Venue - Dates: MediaEval 2015, Wurzen, Germany, 2015-09-13 - 2015-09-14
Organisations: IT Innovation

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Local EPrints ID: 382360
URI: http://eprints.soton.ac.uk/id/eprint/382360
PURE UUID: aab333a5-9600-4c1e-ace6-1fba21e9cf43
ORCID for Stuart Middleton: ORCID iD orcid.org/0000-0001-8305-8176

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Date deposited: 29 Oct 2015 11:33
Last modified: 15 Sep 2021 01:45

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