Abstract
In this chapter, Furman provides an overview on social media monitoring technologies and their relevance to the wider discussions on datafication and informational capitalism. In the age of the informational capitalism, the capacity to leverage emotional commitment is crucial to the valuation of a brand and for attracting a much higher investor valuation on global markets. For this, marketing and public relations strategies have turned to the affordances offered by networked interactive technologies. This has created new opportunities as well as new risks. To control risks associated with using networked interactive technologies to generate engagement, organizations have begun to rely on social media monitoring services (SMM).
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Some categories of organizations that have embraced the datafication paradigm include governments, municipalities, corporations, technology companies and intelligence services (boyd and Crawford 2012, p. 663). Although the concerns regarding how the capacities developed by such organizations will reshape issues such as privacy (Morozov 2013) and surveillance (van Dijck 2014), ethics (Mittelstadt et al. 2016) or even social research (Beaton 2016) are pertinent, datafication has also been fundamentally misunderstood by many. For example, some have mistakenly argued that the capacity to quantify society as data will lead to what has been notoriously called the “end of theory” (Andersson 2008). Algorithms, in their inherently computational (and supposedly objective) approach, will render obsolete more human-oriented forms of social research, thereby eliminating the biases found in such approached. As it has been criticized elsewhere, just much as theoretical paradigms, algorithms are also based around human biases and interpretation (Couldry et al. 2016). Therefore, datafication does not eliminate the human biases from any sort of analytical process but introduces new ones instead.
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Most of the relevant content from interviews has been integrated into the narrative structure to make the chapter more readable in terms of flow.
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For example, a client can put in a query for BoomSonar to just listen to results from google.com.tr rather than google.com.
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For example through the usage of affixes, the word Çekoslovakyalı (Czechoslovakian) can be converted into Çekoslovakyalılaştıramadıklarımızdanmışsınızcasına (meaning “Just like you are said to be one of those that we couldn’t manage to convert to a Czechoslovak”).
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For example, the popular PageRank algorithm used by Google’s search engine for ranking websites has a very specific definition of trustworthiness (Reider 2012). On a network, it tends to favor websites that are in closer proximity to a few popular websites rather than those who are popular in themselves. In other words, PageRank favors those who are popular amongst popular people. As one can easily see, such definition of trustworthiness is structured around the conditions of human experience and epistemology .
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Furman, I. (2018). Algorithms, Dashboards and Datafication: A Critical Evaluation of Social Media Monitoring. In: Bilić , P., Primorac, J., Valtýsson, B. (eds) Technologies of Labour and the Politics of Contradiction. Dynamics of Virtual Work. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-76279-1_5
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