Navigation characteristics of online social networks and search engines users

Dunn, Christopher; Gupta, Manika; Gerber, Alexandre; Spatscheck, Oliver

doi:10.1145/2342549.2342560

Cited by 5 publications

(4 citation statements)

References 9 publications

(8 reference statements)

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“…There are important conclusions to be drawn from this reassessment of the state of combining tracking and survey data. First and foremost, it is crucial to admit that apart from very few exceptions (such as Boase & Ling, 2013, or Dunn, Gupta, Gerber, & Spatscheck, 2012), tracking data should not by default be considered an unbiased source of “true” media exposure. Second, our findings of differential bias depending on the device type suggest that we cannot treat the results from different devices equally.…”

Section: Discussionmentioning

confidence: 99%

Two Half-Truths Make a Whole? On Bias in Self-Reports and Tracking Data

Jürgens

Stark

Magin

2019

Social Science Computer Review

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The pervasive use of mobile information technologies brings new patterns of media usage, but also challenges to the measurement of media exposure. Researchers wishing to, for example, understand the nature of selective exposure on algorithmically driven platforms need to precisely attribute individuals' exposure to specific content. Prior research has used tracking data to show that survey-based self-reports of media exposure are critically unreliable. So far, however, little effort has been invested into assessing the specific biases of tracking methods themselves. Using data from a multi-method study, we show that tracking data from mobile devices is linked to systematic distortions in self-report biases. Further inherent but unobservable sources of bias, along with potential solutions, are discussed.The paper is structured as follows: We first review the existing state of research on the relation between self-reports and tracking data. Building on that literature, we discuss the theoretical and pragmatic limitations in the data collection process of various tracking methods, with a special focus on mobile devices.As that section will reveal, there are numerous potential sources of errors at various stages in the data collection process, which warrant an investigation into biases in tracking data. We go on to show empirically that such biases exist, drawing on original data from a multi-method study comprising survey and tracking data. In order to establish the validity of data and method, we first replicate existing findings of biased selfassessments (RQ 1). Using the differences between participants who provided mobile and/or desktop tracking data, we then show a genuinely new type of bias, namely a differential bias in self-reports of people willing to share mobile tracking data (RQ 2). Finally, we assess the impact of this bias through a simulation exercise (RQ 3), which builds on a realistic statistical model of perceived polarization to show how strong tracking bias will impact results. Literature Review: Self-report Bias, Direction and SourcesA growing list of study designs aims to bypass the insufficient reliability of self-reports by directly capturing trace data of digital media usage through various means (Revilla et al. 2017, Araujo et al. 2017, Vraga et al. 2016, Scharkow 2016. The results show rather consistently that there are strong systematic biases present in self-reports across different devices, settings and operationalizations. An early study that served to draw attention to the issues is Prior's (2009b) investigation of time spent on TV. By comparing survey-based self-assessments to Nielsen people meter data (which are generated from custom tracking devices on TVs, see Napoli 2003), he shows that individuals on average overestimate their TV usage by a factor of three, with younger respondents doing worse. Tapping into an earlier debate in political communication (Price & Zaller 1993), the paper suggests either using alternative methods for measuring exposure or instead focusing on deeper l...

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Section: Discussionmentioning

confidence: 99%

Two Half-Truths Make a Whole? On Bias in Self-Reports and Tracking Data

Jürgens

Stark

Magin

2019

Social Science Computer Review

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“…The Google personalization study by Hannak et al (2013) is a great example of a successful implementation of both strategies. In a similar vein, both Dunn, Gupta, Gerber, and Spatscheck (2012) and Flaxman, Goel, and Rao (2016) determine the effects of personalized content from social networking sites and search engines on the diversity of received content.…”

Section: Second Level Bias: Personalizationmentioning

confidence: 99%

“…Even though the top results for that group were less relevant, participants still favored them over the other entries—demonstrating a general willingness to trust search engines’ ranking decisions. Two innovative studies (Dunn et al, ; Flaxman et al, ) even draw on behavioral data that tracked online visits by users, including the use of intermediaries. Taken together, these allow conclusions about the effect of search engines and social networking sites on the diversity of visited content.…”

Section: The General Intermediary Effects Modelmentioning

confidence: 99%

The Power of Default on Reddit: A General Model to Measure the Influence of Information Intermediaries

Jürgens¹,

Stark²

2017

Policy & Internet

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Information intermediaries like search engines and social networks can represent a source of bias for recipients. Their algorithms, especially concerning filtering, sorting, and personalization, are assumed to skew the representation of news topics and thus create media effects in their own right. Largely as a result of methodological challenges, existing studies have often tested isolated hypotheses without covering the full effects chain from content production to reception by the user. However, the preselection of content performed by intermediaries is only relevant if users choose to rely on it, an assumption that so far lacks broad empirical evidence. Based on a broad review of existing study designs, this article develops a general intermediary effects model. From this model, researchers can derive empirical study designs that capture and differentiate the individual effects of filtering, sorting, and personalization. We then apply the model in an assessment of the news aggregator Reddit's change in capacity to influence its users’ information selection. Using counterfactual time series decomposition, we show that changing the sections of the website included in the start page by default can lead to large changes in attention paid to content. These findings substantiate existing assumptions about the power of information intermediaries to shape the information environments of their users.

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“…Thus, the social interactions are classified in this case in two groups: publicity visible activities or silent activities. Dunn et al (2012) compare the behavior of the websites users through online social networks versus through search engines. For this purpose, web traffic logs from thousands of DSL subscribers are used.…”

Section: Discovery Of User Navigation Patternsmentioning

confidence: 99%

Data Mining for User Modeling

Iglesias

Ledezma

Sanchís

2012

International Journal of Organizational and Collective Intelligence

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Nowadays, the systems that interact with the user, including computers, are improving very quickly. In this improvement, a great goal for the near future is to create systems able to adapt its behavior according to the needs of a particular user. The key element for this purpose is the user model. In this context, a goal of user modeling is to increase the effective use of computers and many other systems that require more sophistication in its interaction with the user. To achieve this goal, it is necessary to know how the users behave. Then, the anticipation of certain aspects of human behavior, such as goals, actions or preferences is possible. In this article, the development of user models using soft computing is presented. The main approaches to different user modeling are reviewed, and the main techniques used to develop user models are discussed. Also, a particular example is explained in detail.

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Navigation characteristics of online social networks and search engines users

Cited by 5 publications

References 9 publications

Two Half-Truths Make a Whole? On Bias in Self-Reports and Tracking Data

Two Half-Truths Make a Whole? On Bias in Self-Reports and Tracking Data

The Power of Default on Reddit: A General Model to Measure the Influence of Information Intermediaries

Data Mining for User Modeling

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