Paying for Likes?

Cristofaro, Emiliano De; Friedman, Arik; Jourjon, Guillaume; Kâafar, Mohamed Ali; Shafiq, M. Zubair

doi:10.1145/2663716.2663729

Cited by 57 publications

(10 citation statements)

References 9 publications

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“…This is where fake accounts are used to increase the number of contributors, views, and likes, and there are plenty of exchanges where you can order everything from likes to comments, see for example Amazon Mechanical Turk [22], qcomment [23], freelancer [24], zbj [25] and buyaccs [26]. The scientific community uses a special term for such exchanges-crowdturfing [17,[27][28][29][30]. On these stock exchanges, anyone can buy the services of robotic accounts.…”

Section: Related Workmentioning

confidence: 99%

Detection of Induced Activity in Social Networks: Model and Methodology

2021

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This paper examines the problem of social media special operations and especially induced support in social media during political election campaigns. The theoretical background of the paper is based on the study fake activity in social networks during pre-election processes and the existing models and methods of detection of such activity. The article proposes a methodology for identifying and diagnosing induced support for a political project. The methodology includes a model of induced activity, an algorithm for segmenting the audience of a political project, and a technique for detecting and diagnosing induced support. The proposed methodology provides identification of network combatants, participants of social media special operations, influencing public opinion in the interests of a political project. The methodology can be used to raise awareness of the electorate, the public, and civil society in general about the presence of artificial activity on the page of a political project.

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Section: Related Workmentioning

confidence: 99%

Detection of Induced Activity in Social Networks: Model and Methodology

2021

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“…Celebrities, politicians, brands, organizations, and governments are also using social media platforms to reach their audiences and amplify their messages (De Cristofaro, Friedman, Jourjon, Kaafar, & Shafiq, 2014;Ikram et al, 2017;Stringhini et al, 2013). Hence, a supply and demand business opportunity has given rise to a "Twitter follower market" to artificially increase audience metrics on the platform (Stringhini et al, 2013).…”

Section: The Relationships Between Legacy Media and Audiences In The mentioning

confidence: 99%

Making up Audience: Media Bots and the Falsification of the Public Sphere

Santini

Salles

Tucci

et al. 2020

Communication Studies

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The purpose of this paper is to discuss if and how Brazilian media outlets make use of automated strategies and artificial intelligence (AI) in order to produce convenient social media metrics about themselves and amplify their relevance on Twitter. We examine how media bots can manipulate online ratings, change social perception of what is relevant and increase engagement with both on-and offline media entities. We extracted three types of data: (i) 530,942 tweets containing at least one URL from Globo Group or Folha Group collected via Twitter API; (ii) URL metadata from 158,690 articles by Globo Group and Folha Group; (iii) Twitter trending topics in Brazil. Profiles that posted links were later sampled and classified using the Botometer. Automated and human accounts were analyzed regarding their posting frequency and speed. In this paper, we assess the hypothesis that the existence of media bots is affecting the Twittersphere in Brazil, where automated accounts, empowered by AI, might be responsible for a substantial share of the links to popular Brazilian media outlets on Twitter. Our research provides quantitative empirical evidence that bots are particularly active in amplifying news media links in the initial moments of spreading. Additionally, automated accounts play an important role in promoting TV broadcast programs in Brazil. Based on these evidences, we discuss the strategies adopted by Brazilian media corporations to sustain their omnipresence online that boosts their online audience.

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“…Identification of fake profiles is one piece of the larger puzzle into determining user identity for the sake of attribution of data. [6][7][8][9][10] The challenge with digital identity lies in the determination of the source of content and its trajectory of dissemination through the SNS. In other words, how do we determine who wrote/posted/created content first and how did it spread or morph along its way on the Internet?…”

Section: Digital Identity Ownership and Monetizationmentioning

confidence: 99%

A Blockchain Enhanced Framework for Social Networking

Murimi

2019

ledger

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Social networking sites have given users unprecedented opportunities for the generation and dissemination of content. A variety of social networking sites exist for different purposes, to afford users a range of anonymous and non-anonymous options for self-expression, and the ability to be a part of a virtual community. These “affordances” enable users to create and share content; however, the ability to partially or wholly detach user identity from the content has resulted in unique challenges for content access and content attribution. This paper proposes a framework for secure, trustworthy social networking that also creates value for user-generated content by using a blockchain-enhanced framework for social networking. This work explains the application of such a framework for collocated spaces of robots and IoT devices and identifies key challenges that result as a consequence of merging social networking sites and blockchain technology.

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Paying for Likes?

Cited by 57 publications

References 9 publications

Detection of Induced Activity in Social Networks: Model and Methodology

Detection of Induced Activity in Social Networks: Model and Methodology

Making up Audience: Media Bots and the Falsification of the Public Sphere

A Blockchain Enhanced Framework for Social Networking

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