Adoção da auditabilidade como proposta para identificar informações falsas em redes sociais

The lack of mechanisms made to check reliability of information on social networks is evidenced by the spread of misinformation and rumors in this kind of system. Given this scenario, the provision of tools to assist users with information auditability on social networks needs an emergencial approach. This article describes a catalog, followed by a guide of features that provide auditability on social networks. The guide contains guidelines for development of funcionalities in order to promote the adoption of auditability and enable users to identify false information and validate content on social networks.

[1]  José Luís Braga,et al.  Verificação de Requisitos de Transparência em Modelos iStar , 2012, WER.

[2]  Cristiano Maciel,et al.  Do Facebook às Ruas - Comunidades em Interação , 2013, WAIHCWS.

[3]  Valeria De Antonellis,et al.  A Linked Data Perspective for Effective Exploration of Web APIs Repositories , 2013, ICWE.

[4]  Julio Cesar Sampaio do Prado Leite,et al.  Exploring Business Process Transparency Concepts , 2007, 15th IEEE International Requirements Engineering Conference (RE 2007).

[5]  Charlotte A. Allen,et al.  RUMORS, URBAN LEGENDS AND INTERNET HOAXES , 2005 .

[6]  Kevin J. Slonka Awareness of malicious social engineering among facebook users , 2014 .

[7]  Lawrence Chung,et al.  Software architecture adaptability: an NFR approach , 2001, IWPSE '01.

[8]  Alexis Papadimitriou,et al.  A generalized taxonomy of explanations styles for traditional and social recommender systems , 2012, Data Mining and Knowledge Discovery.

[9]  Kyomin Jung,et al.  Aspects of Rumor Spreading on a Microblog Network , 2013, SocInfo.

[10]  Filippo Menczer,et al.  Fact-checking Effect on Viral Hoaxes: A Model of Misinformation Spread in Social Networks , 2015, WWW.

[11]  Daniel M. Romero,et al.  Influence and passivity in social media , 2010, ECML/PKDD.

[12]  Julio Cesar Sampaio do Prado Leite,et al.  Software Transparency , 2010, Bus. Inf. Syst. Eng..

[13]  Krishna P. Gummadi,et al.  Towards Detecting Anomalous User Behavior in Online Social Networks , 2014, USENIX Security Symposium.

[14]  Robert E. Wray,et al.  Practical Evaluation of Integrated Cognitive Systems , 2012 .

[15]  Yimin Chen,et al.  Misleading Online Content: Recognizing Clickbait as "False News" , 2015, WMDD@ICMI.

[16]  Carolyn Ball,et al.  What Is Transparency? , 2009 .

[17]  Athanasios V. Vasilakos,et al.  Understanding user behavior in online social networks: a survey , 2013, IEEE Communications Magazine.

[18]  Hwa-Young Jeong,et al.  A System Software Quality Model using DeLone and McLean Model and ISO/IEC 9126 , 2012 .

[19]  Rashi Garg,et al.  Scam-Alert: Characterizing Work from Home Scams on Social Networks , 2015 .