Replication Data for: Partisan Temporal Selective News Avoidance: Evidence from Online Trace Data (doi:10.7910/DVN/BLGFDU)

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Part 1: Document Description
Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Part 5: Other Study-Related Materials
Entire Codebook

Document Description

Citation

Title:

Replication Data for: Partisan Temporal Selective News Avoidance: Evidence from Online Trace Data

Identification Number:

doi:10.7910/DVN/BLGFDU

Distributor:

Harvard Dataverse

Date of Distribution:

2024-12-17

Version:

1

Bibliographic Citation:

Heseltine, Michael; Barnehl, Hennes; Wojcieszak, Magdalena, 2024, "Replication Data for: Partisan Temporal Selective News Avoidance: Evidence from Online Trace Data", https://doi.org/10.7910/DVN/BLGFDU, Harvard Dataverse, V1, UNF:6:6PrvI0cwVEOuPlLVUFjdSg== [fileUNF]

Study Description

Citation

Title:

Replication Data for: Partisan Temporal Selective News Avoidance: Evidence from Online Trace Data

Identification Number:

doi:10.7910/DVN/BLGFDU

Authoring Entity:

Heseltine, Michael (University of Amsterdam)

Barnehl, Hennes (University of California San Diego)

Wojcieszak, Magdalena (University of California Davis)

Producer:

Heseltine, Michael

Distributor:

Harvard Dataverse

Access Authority:

Heseltine, Michael

Depositor:

Heseltine, Michael

Date of Deposit:

2024-06-11

Holdings Information:

https://doi.org/10.7910/DVN/BLGFDU

Study Scope

Keywords:

Social Sciences, News Consumption

Abstract:

Copy directly We assess the phenomenon of partisan temporal selective avoidance, or individuals dynamically altering their news consumption when news is negative toward their in-party and out-party. Using nine months of online behavioral data (27,648,770 visits) from 2,462 Americans paired with machine learning classifications, we examine whether changing daily news sentiment toward in- and out-party (macro-level) and exposure to articles negative toward in- or out-party during one's browsing session (micro-level) influence news use. We test if partisans change their consumption of (a) news overall, (b) partisan outlets, (c) hard vs. soft news, and (d) individual articles. We find support for partisan temporal selective news avoidance; partisans alter the volume, type, and source of news because of changing news sentiment. On the macro-level, partisan asymmetries emerge, and on the micro-level, negative news about either party reduce news browsing length while increasing hard news and negative news visits for both Democrats and Republicans.from abstract in AJPS publication

Notes:

This dataset underwent an independent verification process, complying with the AJPS Verification Policy updated June 2023, which replicated the tables and figures in the primary article. For the supplementary materials, verification was performed solely for the successful execution of the code. The verification process was carried out by the Cornell Center for Social Sciences at Cornell University. <br></br> The associated article has been awarded the Open Materials Badge. Learn more about the Open Practice Badges from the <a href="https://www.cos.io/">Center for Open Science</a>. <br></br> <img src="https://socialsciences.cornell.edu/sites/default/files/2024-04/materials_large_color.png" alt="Open Materials Badge " width="60" height="60"> <br></br> Open Materials Badge

Methodology and Processing

Sources Statement

Data Sources:

Robertson, R. E., Jiang, S., Joseph, K., Friedland, L., Lazer, D., & Wilson, C. (2018). Auditing Partisan Audience Bias within Google Search. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 148:1–148:22. https://doi.org/10.1145/3274417 <br></br> Author-created datasets. See the readme.txt file for the other data sources, complete list of datasets, and codebook.pdf for contents.

Data Access

Disclaimer:

The <i>American Journal of Political Science</i> and the Cornell Center for Social Sciences are not responsible for the accuracy or quality of data uploaded within the <i>AJPS</i> Dataverse, for the use of those data, or for interpretations or conclusions based on their use.

Other Study Description Materials

File Description--f10703633

File: DFB_Val_AJPS.tab

  • Number of cases: 500

  • No. of variables per record: 4

  • Type of File: text/tab-separated-values

Notes:

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Notes:

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Notes:

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Notes:

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Notes:

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  • No. of variables per record: 4

  • Type of File: text/tab-separated-values

Notes:

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File Description--f10703634

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  • No. of variables per record: 5

  • Type of File: text/tab-separated-values

Notes:

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Variable Description

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coef

f10271101 Location:

Summary Statistics: StDev 4.304735717424402; Mean 0.7156479166666668; Valid 24.0; Max. 9.055; Min. -12.174

Variable Format: numeric

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f10271101 Location:

Summary Statistics: Valid 24.0; Min. 0.1509; Max. 4.451; Mean 1.7776729166666667; StDev 1.3630494149691141;

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f10271101 Location:

Summary Statistics: Min. -5.45904; Max. 15.04084; Mean 4.199886833333333; StDev 4.676967427482197; Valid 24.0;

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f10271101 Location:

Summary Statistics: Valid 24.0; Mean -2.768591; Max. 3.91292; Min. -18.88896; StDev 5.427902047993102

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f10271091 Location:

Summary Statistics: StDev 6.079451122043737; Min. -17.27; Mean -0.1010283333333333; Max. 12.004; Valid 24.0

Variable Format: numeric

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f10271091 Location:

Summary Statistics: Max. 5.065; Valid 24.0; Min. 0.159; StDev 1.524122996669439; Mean 1.94471875;

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upper

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Summary Statistics: StDev 5.653766396269165; Min. -9.5672; Max. 18.14664; Valid 24.0; Mean 3.710620416666667;

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Summary Statistics: Mean -3.9126770833333335; Valid 24.0; StDev 7.7331800606803744; Min. -24.9728; Max. 5.86136;

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Summary Statistics: Valid 24.0; Min. -13.652; Mean 0.22654166666666684; StDev 5.024199358738159; Max. 11.011

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Summary Statistics: Min. 0.1383; Max. 4.14; Mean 1.6325408333333333; StDev 1.2583827957478035; Valid 24.0;

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Summary Statistics: Min. -19.93576; StDev 6.254046786189337; Valid 24.0; Max. 5.68176; Mean -2.9732383666666666

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Summary Statistics: Max. 1.0; Mean 0.5; Valid 120.0; StDev 0.5020964452534338; Min. 0.0

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Other Study-Related Materials

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1. Fig 1 Sentiment Plot.R

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2. Fig 2 and 3 Visits Plots.R

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type/x-r-syntax

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3. Macro Models Main.R

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4. Micro Models Main.R

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type/x-r-syntax

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5. Plotting Regression Coefficients.R

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appendix_demographics.R

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type/x-r-syntax

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Codebook.pdf

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application/pdf

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Combined in out Sentiment Models Appendix.R

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type/x-r-syntax

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DSentiment.csv

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text/csv

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Macro Month FE Models Appendix.R

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type/x-r-syntax

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NewsVisits.csv

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text/csv

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NewsVisits_Micro.csv

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text/csv

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Readme.txt

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text/plain

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Respondent_Info.rds

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application/gzip

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RSentiment.csv

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text/csv

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Sentiment Probability Models Appendix.R

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US_census_acs.xlsx

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application/vnd.openxmlformats-officedocument.spreadsheetml.sheet

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Validation Tables AJPS.ipynb

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application/x-ipynb+json