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
September 2015
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.
14 - 15 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
More information
Published date: September 2015
Venue - Dates:
MediaEval 2015, Wurzen, Germany, 2015-09-14 - 2015-09-15
Organisations:
IT Innovation
Identifiers
Local EPrints ID: 382360
URI: http://eprints.soton.ac.uk/id/eprint/382360
PURE UUID: aab333a5-9600-4c1e-ace6-1fba21e9cf43
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Date deposited: 29 Oct 2015 11:33
Last modified: 15 Mar 2024 03:08
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