Serf and turf

Gang, Wang; Wilson, Christo; Zhao, Xiaohan; Zhu, Yibo; Mohanlal, Manish; Zheng, Haitao; Zhao, Ben Y.

doi:10.1145/2187836.2187928

Cited by 155 publications

(14 citation statements)

References 16 publications

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“…The manipulative effects, agendas, and ecosystem of generalized social media marketing campaigns have been identified and extensively studied [ 29 – 31 ]. Other work, [ 32 ], has distinguished between purely automated accounts, or “robots”, and human assisted automated accounts referred to as “cyborgs”.…”

Section: Introductionmentioning

confidence: 99%

Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter

et al. 2016

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BackgroundTwitter has become the “wild-west” of marketing and promotional strategies for advertisement agencies. Electronic cigarettes have been heavily marketed across Twitter feeds, offering discounts, “kid-friendly” flavors, algorithmically generated false testimonials, and free samples.MethodsAll electronic cigarette keyword related tweets from a 10% sample of Twitter spanning January 2012 through December 2014 (approximately 850,000 total tweets) were identified and categorized as Automated or Organic by combining a keyword classification and a machine trained Human Detection algorithm. A sentiment analysis using Hedonometrics was performed on Organic tweets to quantify the change in consumer sentiments over time. Commercialized tweets were topically categorized with key phrasal pattern matching.ResultsThe overwhelming majority (80%) of tweets were classified as automated or promotional in nature. The majority of these tweets were coded as commercialized (83.65% in 2013), up to 33% of which offered discounts or free samples and appeared on over a billion twitter feeds as impressions. The positivity of Organic (human) classified tweets has decreased over time (5.84 in 2013 to 5.77 in 2014) due to a relative increase in the negative words ‘ban’, ‘tobacco’, ‘doesn’t’, ‘drug’, ‘against’, ‘poison’, ‘tax’ and a relative decrease in the positive words like ‘haha’, ‘good’, ‘cool’. Automated tweets are more positive than organic (6.17 versus 5.84) due to a relative increase in the marketing words like ‘best’, ‘win’, ‘buy’, ‘sale’, ‘health’, ‘discount’ and a relative decrease in negative words like ‘bad’, ‘hate’, ‘stupid’, ‘don’t’.ConclusionsDue to the youth presence on Twitter and the clinical uncertainty of the long term health complications of electronic cigarette consumption, the protection of public health warrants scrutiny and potential regulation of social media marketing.

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Section: Introductionmentioning

confidence: 99%

Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter

et al. 2016

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“…However, spammers use platform‐specific techniques to elude spam detection systems. Recently, crowdsourcing systems have also been exploited to post malicious content (Lee, Eoff, & Caverlee, ; Wang et al, ). Moreover, spammers continue changing their strategies to remain undetected from spam detection systems (Chinavle, Kolari, Oates, & Finin, ).…”

Section: Related Workmentioning

confidence: 99%

“…However, such platforms have been exploited by spammers to generate and propagate malicious campaigns and rumors. A study on the crowdsourcing sites reveals that ∼90% of the tasks are for crowdturfing (Wang et al, ). The types of malicious tasks and properties of requesters and workers on crowdsourcing sites have been analyzed (Lee, Tamilarasan, & Caverlee, ).…”

Section: Related Workmentioning

confidence: 99%

An analysis of 14 Million tweets on hashtag‐oriented spamming*

Sedhai

Sun

2017

Asso for Info Science & Tech

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Over the years, Twitter has become a popular platform for information dissemination and information gathering. However, the popularity of Twitter has attracted not only legitimate users but also spammers who exploit social graphs, popular keywords, and hashtags for malicious purposes. In this paper, we present a detailed analysis of the HSpam14 dataset, which contains 14 million tweets with spam and ham (i.e., nonspam) labels, to understand spamming activities on Twitter. The primary focus of this paper is to analyze various aspects of spam on Twitter based on hashtags, tweet contents, and user profiles, which are useful for both tweet‐level and user‐level spam detection. First, we compare the usage of hashtags in spam and ham tweets based on frequency, position, orthography, and co‐occurrence. Second, for content‐based analysis, we analyze the variations in word usage, metadata, and near‐duplicate tweets. Third, for user‐based analysis, we investigate user profile information. In our study, we validate that spammers use popular hashtags to promote their tweets. We also observe differences in the usage of words in spam and ham tweets. Spam tweets are more likely to be emphasized using exclamation points and capitalized words. Furthermore, we observe that spammers use multiple accounts to post near‐duplicate tweets to promote their services and products. Unlike spammers, legitimate users are likely to provide more information such as their locations and personal descriptions in their profiles. In summary, this study presents a comprehensive analysis of hashtags, tweet contents, and user profiles in Twitter spamming.

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“…Persona management software makes it possible for one person to manage dozens of fake identities in online discussions and social media sites as though each identity is a unique person (Wang et al 2012). A few people using persona management software can then coordinate what appears to be a significant groundswell of challenges or support for a persona, organization, or policy position.…”

Section: Persona Managementmentioning

confidence: 99%

“…Those who specialize in providing false content for hire are known as cyber shills. Through the use of big data, persona management systems, and the use of crowdsourcing platforms, it has become possible to manage large teams of cyber shills to coordinate massive astroturf campaigns-crowdturfing (Wang et al 2012). It is possible for individuals to make a career as a professional cyber shill for hire in carrying out astroturf campaigns, according to Wang et al…”

Section: Persona Managementmentioning

confidence: 99%

The Communicative Work of Organizations in Shaping Argumentative Realities

Aakhus

2016

Philos. Technol.

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It is argued here that large-scale organization and networked computing enable new divisions of communicative work aimed at shaping the content, direction, and outcomes of societal conversations. The challenge for argumentation theory and practice lies in attending to these new divisions of communicative work in constituting contemporary argumentative realities. Goffman's conceptualization of participation frameworks and production formats are applied to articulate the communicative work of organizations afforded by networked computing that invents and innovates argument in all of its senses-as product, process, and procedure. Communicative work, however, may be scaffolding argumentative contexts and practices that are quite different than what has constituted past argumentative realities. The computerization of argument happens as organizations invent and innovate argument practice relative to the demands and opportunities of interorganizational communication. The cases and examples examined here suggest that argument practice is evolving around the logic of conversation and the principle of personalization. The analysis challenges argumentation theory to seriously engage with the construction of communicative contexts and the realization of ideas about disagreement management in organizational practice and information infrastructures. Directions for integrating insights from a design perspective on argument with insights from organizational and information systems theory are proposed for coming to terms with an era of large-scale organization and computerization, in particular the evolution of argument practice, the inscription of argument in the built environment, and the absorption of socio-cultural argument practices by organizations and computation.

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Serf and turf

Cited by 155 publications

References 16 publications

Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter

Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter

An analysis of 14 Million tweets on hashtag‐oriented spamming*

The Communicative Work of Organizations in Shaping Argumentative Realities

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