@article{Boididou2017,

title = {Verifying information with multimedia content on twitter: A comparative study of automated approaches},

author = {Christina Boididou and Stuart Middleton and Zhiwei Jin and Symeon Papadopoulos and Duc-Tien Dang-Nguyen and G. Boato and Ioannis (Yiannis) Kompatsiaris},

url = {https://www.researchgate.net/publication/319859894_Verifying_information_with_multimedia_content_on_twitter_A_comparative_study_of_automated_approaches},

doi = {10.1007/s11042-017-5132-9},

year  = {2017},

date = {2017-09-01},

urldate = {2017-09-01},

journal = {Multimedia Tools and Applications},

volume = {77},

number = {12},

pages = {15545-15571},

abstract = {An increasing amount of posts on social media are used for dissem- inating 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 eect on their credibility. To avoid such eects, 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

},

note = {Pre SFI},

keywords = {Credibility, Fake Detection, Multimedia, Social Media, Trust, Twitter, Veracity, Verification, WP3: Media Content Production and Analysis},

pubstate = {published},

tppubtype = {article}

}