Is There Personalization in Twitter Search? A Study on polarized opinions about the Brazilian Welfare Reform

Santos, Jônatas Castro dos; Siqueira, Sean W. M.; Nunes, Bernardo Pereira; Balestrassi, Pedro Paulo; Pereira, Fabrício Raphael Silva

doi:10.1145/3394231.3397917

Cited by 4 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, concerns about the possible negative effects of personalised online services have led to efforts to quantify their degree of personalisation. For example, personalisation in web search as well as online news has been measured by using similarity ratios (Cozza et al, 2016; Dos Santos et al, 2020; Hannák et al, 2017; Krafft et al, 2019; Le et al, 2019; Puschmann, 2018; Salehi et al, 2015). Similarity ratios have also been applied to measuring targeted online advertising (Balebako et al, 2012; Guha et al, 2010).…”

Section: A Novel Metric: the Capimentioning

confidence: 99%

The Concentration-after-Personalisation Index (CAPI): Governing effects of personalisation using the example of targeted online advertising

Laux¹,

Stephany

Russell³

et al. 2022

Big Data & Society

View full text Add to dashboard Cite

Firms are increasingly personalising their offers and services, leading to an ever finer-grained segmentation of consumers online. Targeted online advertising and online price discrimination are salient examples of this development. While personalisation's overall effects on consumer welfare are expectably ambiguous, it can lead to concentration in the distribution of advertising and commercial offers. Constellations are possible in which a market is generally open to competition, but the targeted consumer is only made aware of one possible seller. For the consumer, such a market could effectively resemble a monopoly. We call such extreme cases ‘targeting pockets’. Competition-law metrics such as the Herfindahl–Hirschman Index and traditional means of public oversight of adverts would not detect this concentration. We, therefore, suggest a novel metric, the Concentration-after-Personalisation Index (CAPI). The CAPI treats every consumer as a separate ‘market’, computes a measure of concentration for personalised adverts and offers for each individual consumer separately, and then averages the result to measure the exposure experienced by an average consumer. We demonstrate how the CAPI can serve as a monitoring tool for regulators and auditors and thus help to enforce existing consumer law as well as proposed new regulations such as the European Union's Digital Services Act and its Artificial Intelligence Act. We further show how adding noise via randomly distributed non-personalised adverts can dilute the potential harm of overly concentrated personalisation. We demonstrate how the CAPI can identify the optimal degree of added noise, balancing the protection of consumer choice with the economic interests of advertisers.

show abstract