The extent and nature of ideological selective exposure online: Combining survey responses with actual web log data from the 2013 Israeli Elections

Dvir-Gvirsman, Shira; Tsfati, Yariv; Menchen-Trevino, Ericka

doi:10.1177/1461444814549041

Cited by 86 publications

(71 citation statements)

References 37 publications

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“…Specifically, the effectiveness of providing population averages as anchors for duration measures should be studied. Applying multi-method approaches instead of relying on survey data only is another way to enhance the possibilities of survey and tracking data also allowing to directly asses response inaccuracies (e.g., Dvir-Gvirsman, Tsfati, & Menchen-Trevino, 2016).…”

Section: Recommendationsmentioning

confidence: 99%

How Much Time Do You Spend Online? Understanding and Improving the Accuracy of Self-Reported Measures of Internet Use

Araujo

Wonneberger

Neijens

et al. 2017

Communication Methods and Measures

183

149

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How much time do you spend online? Understanding and improving the accuracy of self-reported measures of internet use Araujo, T.B.; Wonneberger, A.; Neijens, P.C.; de Vreese, C.H. : 10.1080/19312458.2017.1317337 Link to publication Published in: Communication Methods and Measures DOI Citation for published version (APA):Araujo, T., Wonneberger, A., Neijens, P., & de Vreese, C. (2017). How much time do you spend online? Understanding and improving the accuracy of self-reported measures of internet use. Communication Methods and Measures, 11(3), 173-190. DOI: 10.1080173-190. DOI: 10. /19312458.2017 General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Given the importance of survey measures of online media use for communication research, it is crucial to assess and improve their quality, in particular because the increasingly fragmented and ubiquitous usage of internet complicates the accuracy of self-reported measures. This study contributes to the discussion regarding the accuracy of self-reported internet use by presenting relevant factors potentially affecting biases of self-reports and testing survey design strategies to improve accuracy. Combining automatic tracking data and survey data from the same participants (N = 690) confirmed low levels of accuracy and tendencies of over-reporting. The analysis revealed biases due to a range of factors associated with the intensity of (actual) internet usage, propensity to multitask, day of reference, and the usage of mobile devices. An anchoring technique could not be proved to reduce inaccuracies of reporting behavior. Several recommendations for research practice follow from these findings.

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Section: Recommendationsmentioning

confidence: 99%

How Much Time Do You Spend Online? Understanding and Improving the Accuracy of Self-Reported Measures of Internet Use

Araujo

Wonneberger

Neijens

et al. 2017

Communication Methods and Measures

183

149

View full text Add to dashboard Cite

show abstract

“…For obvious reasons, this presents formidable methodological challengestracking one person's complete media intake over time is difficult enough, and most researchers would want to track multiple individuals. To my knowledge, no study has yet been able to accomplish this in a rigorous way, although Dvir-Gvirsman et al (2016) come the closest for online content sources. Consequently, much of the research that purports to examine personalized information environments analyzes only one platform or medium.…”

Section: Methodological Challengesmentioning

confidence: 99%

“…Most empirical work on the topic suggests that neither of these assumptions generally hold. Instead, people tend to exhibit moderate to strong preferences for ideologically-consistent content while failing to routinely reject content from the other side (Bakshy, Messing, & Adamic, 2015;Barberá, Jost, Nagler, Tucker, & Bonneau, 2015;Dvir-Gvirsman, Tsfati, & Menchen-Trevino, 2016;Garrett, 2009;Gentzkow & Shapiro, 2011;Hanna et al, 2013;Hargittai, Gallo, & Kane, 2008;Kobayashi & Ikeda, 2009). Some studies have found differences in the degrees of ideological self-segregation between liberals and conservatives (Barberá et al, 2015;Boutyline & Willer, 2017;Hanna et al, 2013) as well as between political issues (Bakshy et al, 2015;Barberá et al, 2015;Himelboim, McCreery, & Smith, 2013).…”

Section: Do Echo Chambers Exist?mentioning

confidence: 99%

Misinformation and Identity-Protective Cognition

Kahan

2017

SSRN Journal

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“…This may seem like an un-controversial topic, particularly in the Trump era. However, there have been decades-long controversies about the extent and nature of selective exposure (Feldman, Stroud, Bimber, & Wojcieszak, 2013;Dvir-Gvirsman, Tsfati, & Menchen-Trevino, 2014;Hart et al, 2009;Sears & Freedman, 1967).…”

Section: Trace Partnership: Collecting Real-world Behaviors and Self mentioning

confidence: 99%

A Division of Labor: The Role of Big Data Analysis in the Repertoire of Internet Research Methods

Helles

Ørmen

Jensen

et al. 2020

SPIR

Self Cite

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In recent years, large-scale analysis of log data from digital devices - often termed ""big data analysis"" (Lazer, Kennedy, King, & Vespignani, 2014) - have taken hold in the field of internet research. Through Application Programming Interfaces (APIs) and commercial measurement, scholars have been able to analyze social media users (Freelon 2014) and web audiences (Taneja, 2016) on an uprecedented scale. And by developing digital research tools, scholars have been able to track individuals across websites (Menchen-Trevino, 2013) and mobile applications (Ørmen & Thorhauge 2015) in greater detail than ever before. Big data analysis holds unique potential for studying communication in depth and across many individuals (see e.g. Boase & Ling, 2013; Prior, 2013). At the same time, this approach introduces new methodological challenges in the transparency of data collection (Webster, 2014), sampling of participants and validity of conclusions (Rieder, Abdulla, Poell, Woltering, & Zack, 2015). Firstly, data aggregation is typically designed for commercial rather than academic purposes. The type of data included as well as how it is presented depend in large part on the business interests of measurement and advertisement companies (Webster, 2014). Secondly, when relying on this kind of secondary data it can be difficult to validate the output or techniques used to generate the data (Rieder, Abdulla, Poell, Woltering, & Zack, 2015). Thirdly, often the unit of analysis is media-centric, taking specific websites or social network pages as the empirical basis instead of individual users (Taneja, 2016). This makes it hard to untangle the behavior of real-world users from the aggregate trends. Lastly, variations in what users do might be so large that it is necessary to move from the aggregate to smaller groups of users to make meaningful inferences (Welles, 2014). Internet research is thus faced with a new research approach in big data analysis with potentials and perils that need to be discussed in combination with traditional approaches. This panel explores the role of big data analysis in relation to the wider repertoire of methods in internet research. The panel comprises four presentations that each sheds light on the complementarity of big data analysis with more traditional qualitative and quantitative methods. The first presentation opens the discussion with an overview of strategies for combining digital traces and commercial audience data with qualitative interviews and quantitative survey methods. The next presentation explores the potential of trace data to improve upon the experimental method. Researcher-collected data enables scholars to operate in a real-world setting, in contrast to a research lab, while obtaining informed consent from participants. The third presentation argues that large-scale audience data provide a unique perspective on internet use. By integrating census-level information about users with detailed traces of their behavior across websites, commercial audience data combines the strength of surveys and digital trace data respectively. Lastly, the fourth presentation shows how multi-institutional collaboration makes it possible do document social media activity (on Twitter) for a whole country (Australia) in a comprehensive manner. A feat not possible through other methods on a similar scale. Through these four presentations, the panel aims to situate big data analysis in the broader repertoire of internet research methods.

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The extent and nature of ideological selective exposure online: Combining survey responses with actual web log data from the 2013 Israeli Elections

Cited by 86 publications

References 37 publications

How Much Time Do You Spend Online? Understanding and Improving the Accuracy of Self-Reported Measures of Internet Use

How Much Time Do You Spend Online? Understanding and Improving the Accuracy of Self-Reported Measures of Internet Use

Misinformation and Identity-Protective Cognition

A Division of Labor: The Role of Big Data Analysis in the Repertoire of Internet Research Methods

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