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Verifying information with multimedia content on twitter: A comparative study of automated approaches

Verifying information with multimedia content on twitter: A comparative study of automated approaches
Verifying information with multimedia content on twitter: A comparative study of automated approaches
An increasing amount of posts on social media are used for disseminating news information and are accompanied by multimedia content. Such
content may often be misleading or be digitally manipulated. More often than
not, such pieces of content reach the front pages of major news outlets, having
a detrimental effect on their credibility. To avoid such effects, there is profound
need for automated methods that can help debunk and verify online content
in very short time. To this end, we present a comparative study of three such
methods that are catered for Twitter, a major social media platform used for
news sharing. Those include: a) a method that uses textual patterns to extract

An increasing amount of posts on social media are used for disseminating news information and are accompanied by multimedia content. Such content may often be misleading or be digitally manipulated. More often than not, such pieces of content reach the front pages of major news outlets, having a detrimental effect on their credibility. To avoid such effects, there is profound need for automated methods that can help debunk and verify online content in very short time. To this end, we present a comparative study of three such methods that are catered for Twitter, a major social media platform used for news sharing. Those include: a) a method that uses textual patterns to extract claims about whether a tweet is fake or real and attribution statements about the source of the content; b) a method that exploits the information that same-topic tweets should be also similar in terms of credibility; and c) a method that uses a semi-supervised learning scheme that leverages the decisions of two independent credibility classifiers. We perform a comprehensive comparative evaluation of these approaches on datasets released by the Verifying Multimedia Use (VMU) task organized in the context of the 2015 and 2016 MediaEval benchmark. In addition to comparatively evaluating the three presented methods, we devise and evaluate a combined method based on their outputs, which outperforms all three of them. We discuss these findings and provide insights to guide future generations of verification tools for media professionals.
Multimedia, Fake detection, Verification , Credibility , Veracity , Trust , Social media , Twitter
1380-7501
Boididou, Christina
46e6579d-dab2-4d25-97f7-174775642f17
Middleton, Stuart E.
404b62ba-d77e-476b-9775-32645b04473f
Jin, Zhiwei
3a120608-856f-447b-99cd-9b4e9c84a9d6
Papadopoulos, Symeon
818a6f28-8102-45b4-8e95-53be585ec20a
Dang-Nguyen, Duc-Tien
94dd623b-ab1a-4a1c-a532-165ac612eec8
Boato, Giulia
1542ec1a-7d42-4a0a-96ce-6b9b454684f5
Kompatsiaris, Yiannis
364cc081-661c-4f71-b6e0-025b02c25592
Boididou, Christina
46e6579d-dab2-4d25-97f7-174775642f17
Middleton, Stuart E.
404b62ba-d77e-476b-9775-32645b04473f
Jin, Zhiwei
3a120608-856f-447b-99cd-9b4e9c84a9d6
Papadopoulos, Symeon
818a6f28-8102-45b4-8e95-53be585ec20a
Dang-Nguyen, Duc-Tien
94dd623b-ab1a-4a1c-a532-165ac612eec8
Boato, Giulia
1542ec1a-7d42-4a0a-96ce-6b9b454684f5
Kompatsiaris, Yiannis
364cc081-661c-4f71-b6e0-025b02c25592

Boididou, Christina, Middleton, Stuart E., Jin, Zhiwei, Papadopoulos, Symeon, Dang-Nguyen, Duc-Tien, Boato, Giulia and Kompatsiaris, Yiannis (2017) Verifying information with multimedia content on twitter: A comparative study of automated approaches. Multimedia Tools and Applications. (doi:10.1007/s11042-017-5132-9).

Record type: Article

Abstract

An increasing amount of posts on social media are used for disseminating news information and are accompanied by multimedia content. Such
content may often be misleading or be digitally manipulated. More often than
not, such pieces of content reach the front pages of major news outlets, having
a detrimental effect on their credibility. To avoid such effects, there is profound
need for automated methods that can help debunk and verify online content
in very short time. To this end, we present a comparative study of three such
methods that are catered for Twitter, a major social media platform used for
news sharing. Those include: a) a method that uses textual patterns to extract

An increasing amount of posts on social media are used for disseminating news information and are accompanied by multimedia content. Such content may often be misleading or be digitally manipulated. More often than not, such pieces of content reach the front pages of major news outlets, having a detrimental effect on their credibility. To avoid such effects, there is profound need for automated methods that can help debunk and verify online content in very short time. To this end, we present a comparative study of three such methods that are catered for Twitter, a major social media platform used for news sharing. Those include: a) a method that uses textual patterns to extract claims about whether a tweet is fake or real and attribution statements about the source of the content; b) a method that exploits the information that same-topic tweets should be also similar in terms of credibility; and c) a method that uses a semi-supervised learning scheme that leverages the decisions of two independent credibility classifiers. We perform a comprehensive comparative evaluation of these approaches on datasets released by the Verifying Multimedia Use (VMU) task organized in the context of the 2015 and 2016 MediaEval benchmark. In addition to comparatively evaluating the three presented methods, we devise and evaluate a combined method based on their outputs, which outperforms all three of them. We discuss these findings and provide insights to guide future generations of verification tools for media professionals.

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Verifying Information with Multimedia Content on - Accepted Manuscript
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More information

Accepted/In Press date: 20 August 2017
e-pub ahead of print date: 15 September 2017
Additional Information: AM now added.
Keywords: Multimedia, Fake detection, Verification , Credibility , Veracity , Trust , Social media , Twitter

Identifiers

Local EPrints ID: 414872
URI: http://eprints.soton.ac.uk/id/eprint/414872
ISSN: 1380-7501
PURE UUID: 29f5eca7-84e0-4db1-a512-1c1fbb28dccd
ORCID for Stuart E. Middleton: ORCID iD orcid.org/0000-0001-8305-8176

Catalogue record

Date deposited: 12 Oct 2017 16:31
Last modified: 17 Dec 2019 05:53

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Contributors

Author: Christina Boididou
Author: Stuart E. Middleton ORCID iD
Author: Zhiwei Jin
Author: Symeon Papadopoulos
Author: Duc-Tien Dang-Nguyen
Author: Giulia Boato
Author: Yiannis Kompatsiaris

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