667 lines
20 KiB
YAML
667 lines
20 KiB
YAML
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---
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preprocessing:
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data_order0:
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- method: data_order
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param: 0
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data_order1:
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- method: data_order
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param: 1
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data_politics:
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- method: data_order
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param: 0
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- method: data_section
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param: 'Politics'
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data_foreign_affairs:
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- method: data_order
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param: 0
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- method: data_section
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param: 'Foreign affairs'
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data_science:
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- method: data_order
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param: 0
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- method: data_section
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param: 'Science'
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data_economy:
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- method: data_order
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param: 0
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- method: data_section
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param: 'Economy'
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data_miscellaneous:
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- method: data_order
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param: 0
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- method: data_section
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param: 'Miscellaneous'
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data_culture:
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- method: data_order
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param: 0
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- method: data_section
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param: 'Culture'
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data_sports:
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- method: data_order
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param: 0
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- method: data_section
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param: 'Sports'
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data_mobility:
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- method: data_order
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param: 0
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- method: data_section
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param: 'Mobility'
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data_internet:
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- method: data_order
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param: 0
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- method: data_section
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param: 'Internet'
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data_health:
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- method: data_order
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param: 0
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- method: data_section
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param: 'Health'
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data_order0_with_minimum_one_vote:
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- method: data_order
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param: 0
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- method: exclude_data_with_value
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param: {'column': 'totalvotes', 'value': 0}
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descriptive:
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- !descriptive_overview
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name: "Extended_Data_Table_1_Descriptive_Data_for_different_comment_levels"
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dataset: "data"
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group_by: "order"
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metrics:
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- operation: "count"
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column: null
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- operation: "count_nonzero"
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column: "totalvotes"
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- operation: "sum"
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column: "totalvotes"
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- operation: "mean"
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column: "totalvotes"
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- operation: "std_dev"
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column: "totalvotes"
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- operation: "sum"
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column: "upvotes"
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- operation: "sum"
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column: "downvotes"
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- operation: "mean"
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column: "bayes-corrected (q=0.25) valence"
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- operation: "std_dev"
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column: "bayes-corrected (q=0.25) valence"
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- operation: "mean"
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column: "bayes-corrected (q=0.25) extremity"
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- operation: "std_dev"
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column: "bayes-corrected (q=0.25) extremity"
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- !descriptive_overview
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name: "Extended_Data_Table_2_Descriptive_Data_for_different_news_categories"
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dataset: "data"
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group_by: "section"
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metrics:
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- operation: "count"
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column: null
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- operation: "sum"
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column: "number O(n+1)-replies"
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- operation: "count_nonzero"
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column: "number O(n+1)-replies"
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- operation: "count_nonzero"
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column: "totalvotes"
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- operation: "sum"
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column: "totalvotes"
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- operation: "sum"
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column: "upvotes"
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- operation: "sum"
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column: "downvotes"
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- operation: "count_nonzero"
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column: "totalvotes"
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- operation: "mean"
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column: "valence"
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- operation: "std_dev"
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column: "valence"
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- operation: "mean"
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column: "bayes-corrected (q=0.25) valence"
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- operation: "std_dev"
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column: "bayes-corrected (q=0.25) valence"
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- operation: "mean"
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column: "extremity"
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- operation: "std_dev"
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column: "extremity"
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- operation: "mean"
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column: "bayes-corrected (q=0.25) extremity"
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- operation: "std_dev"
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column: "bayes-corrected (q=0.25) extremity"
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analysis:
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- !linear_regression
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name: "Evidence_uncongeniality_simplest_model_linear_regression_only_valence_non_standardized"
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dataset: "data_order0"
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independent_variables:
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- 'valence'
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dependent_variable: 'number O(n+1)-replies'
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standardize: false
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report_effect_size: true
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- !linear_regression
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name: "Evidence_uncongeniality_preregistered_model"
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dataset: "data_order0"
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independent_variables:
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- 'bayes-corrected (q=0.25) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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report_effect_size: true
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- !linear_regression
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name: "Evidence_uncongeniality_stability_against_variation_in_weight_q5"
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dataset: "data_order0"
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independent_variables:
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- 'bayes-corrected (q=0.5) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression
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name: "Evidence_uncongeniality_stability_against_variation_in_weight_q75"
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dataset: "data_order0"
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independent_variables:
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- 'bayes-corrected (q=0.75) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression
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name: "Evidence_uncongeniality_stability_against_variation_in_weight__no_bayes_correction"
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dataset: "data_order0"
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independent_variables:
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- 'valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression_grouped
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name: "Evidence_uncongeniality_robustness_analysis_on_person_level"
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dataset: "data_order0"
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independent_variables:
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- 'bayes-corrected (q=0.25) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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aggregation_functions:
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- 'mean'
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- 'sum'
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- 'sum'
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group_by: 'user_id'
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standardize: true
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print_detailed_coefficients: true
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- !linear_regression_grouped
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name: "Evidence_uncongeniality_robustness_analysis_on_section_level"
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dataset: "data_order0"
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independent_variables:
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- 'bayes-corrected (q=0.25) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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aggregation_functions:
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- 'mean'
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- 'sum'
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- 'sum'
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group_by: 'section'
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standardize: true
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print_detailed_coefficients: true
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- !linear_regression
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name: "Evidence_uncongenialty_section_politics"
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dataset: "data_politics"
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independent_variables:
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- 'bayes-corrected (q=0.25) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression
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name: "Evidence_uncongenialty_section_affairs"
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dataset: "data_foreign_affairs"
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independent_variables:
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- 'bayes-corrected (q=0.25) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression
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name: "Evidence_uncongenialty_section_science"
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dataset: "data_science"
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independent_variables:
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- 'bayes-corrected (q=0.25) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression
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name: "Evidence_uncongenialty_section_economy"
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dataset: "data_economy"
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independent_variables:
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- 'bayes-corrected (q=0.25) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression
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name: "Evidence_uncongenialty_section_miscellaneous"
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dataset: "data_miscellaneous"
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independent_variables:
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- 'bayes-corrected (q=0.25) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression
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name: "Evidence_uncongenialty_section_culture"
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dataset: "data_culture"
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independent_variables:
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- 'bayes-corrected (q=0.25) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression
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name: "Evidence_uncongenialty_section_sports"
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dataset: "data_sports"
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independent_variables:
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- 'bayes-corrected (q=0.25) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression
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name: "Evidence_uncongenialty_section_mobility"
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dataset: "data_mobility"
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independent_variables:
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- 'bayes-corrected (q=0.25) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression
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name: "Evidence_uncongenialty_section_internet"
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dataset: "data_internet"
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independent_variables:
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- 'bayes-corrected (q=0.25) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression
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name: "Evidence_uncongenialty_section_health"
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dataset: "data_health"
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independent_variables:
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- 'bayes-corrected (q=0.25) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression
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name: "Evidence_uncongeniality_robustness_order1"
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dataset: "data_order1"
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independent_variables:
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- 'bayes-corrected (q=0.25) valence'
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- 'totalvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression
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name: "Evidence_uncogeniality_model_with_seperate_upvotes_downvotes"
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dataset: "data_order0"
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independent_variables:
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- 'upvotes'
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- 'downvotes'
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dependent_variable: 'number O(n+1)-replies'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_preregistered_model"
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dataset: "data_order0"
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independent_variables:
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- 'mean bayes-corrected (q=0.25) valence of replies'
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dependent_variable: 'bayes-corrected (q=0.25) valence'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_stability_against_variation_in_weight_q5"
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dataset: "data_order0"
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independent_variables:
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- 'mean bayes-corrected (q=0.5) valence of replies'
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dependent_variable: 'bayes-corrected (q=0.5) valence'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_stability_against_variation_in_weight_q75"
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dataset: "data_order0"
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independent_variables:
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- 'mean bayes-corrected (q=0.75) valence of replies'
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dependent_variable: 'bayes-corrected (q=0.75) valence'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_stability_against_variation_in_weight_no_bayes_correction"
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dataset: "data_order0"
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independent_variables:
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- 'mean valence of replies'
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dependent_variable: 'valence'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_section_politics"
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dataset: "data_politics"
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independent_variables:
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- 'mean bayes-corrected (q=0.25) valence of replies'
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dependent_variable: 'bayes-corrected (q=0.25) valence'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_section_affairs"
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dataset: "data_foreign_affairs"
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independent_variables:
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- 'mean bayes-corrected (q=0.25) valence of replies'
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dependent_variable: 'bayes-corrected (q=0.25) valence'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_section_science"
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dataset: "data_science"
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independent_variables:
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- 'mean bayes-corrected (q=0.25) valence of replies'
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dependent_variable: 'bayes-corrected (q=0.25) valence'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_section_economy"
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dataset: "data_economy"
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independent_variables:
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- 'mean bayes-corrected (q=0.25) valence of replies'
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dependent_variable: 'bayes-corrected (q=0.25) valence'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_section_miscellaneous"
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dataset: "data_miscellaneous"
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independent_variables:
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- 'mean bayes-corrected (q=0.25) valence of replies'
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dependent_variable: 'bayes-corrected (q=0.25) valence'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_section_culture"
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dataset: "data_culture"
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independent_variables:
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- 'mean bayes-corrected (q=0.25) valence of replies'
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dependent_variable: 'bayes-corrected (q=0.25) valence'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_section_sports"
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dataset: "data_sports"
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independent_variables:
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- 'mean bayes-corrected (q=0.25) valence of replies'
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dependent_variable: 'bayes-corrected (q=0.25) valence'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_section_mobility"
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dataset: "data_mobility"
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independent_variables:
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- 'mean bayes-corrected (q=0.25) valence of replies'
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dependent_variable: 'bayes-corrected (q=0.25) valence'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_section_internet"
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dataset: "data_internet"
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independent_variables:
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- 'mean bayes-corrected (q=0.25) valence of replies'
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dependent_variable: 'bayes-corrected (q=0.25) valence'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_section_health"
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dataset: "data_health"
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independent_variables:
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- 'mean bayes-corrected (q=0.25) valence of replies'
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dependent_variable: 'bayes-corrected (q=0.25) valence'
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standardize: true
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- !linear_regression
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name: "Evidence_antagonism_robustness_order1"
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dataset: "data_order1"
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independent_variables:
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||
|
- 'mean bayes-corrected (q=0.25) valence of replies'
|
||
|
dependent_variable: 'bayes-corrected (q=0.25) valence'
|
||
|
standardize: true
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_paired_ttest_extremity"
|
||
|
dataset: "data_order0"
|
||
|
variable_1: 'bayes-corrected (q=0.25) extremity'
|
||
|
variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_stability_against_variation_in_weight_paired_ttest_q5"
|
||
|
dataset: "data_order0"
|
||
|
variable_1: 'bayes-corrected (q=0.5) extremity'
|
||
|
variable_2: 'mean bayes-corrected (q=0.5) extremity of replies'
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_stability_against_variation_in_weight_paired_ttest_q75"
|
||
|
dataset: "data_order0"
|
||
|
variable_1: 'bayes-corrected (q=0.75) extremity'
|
||
|
variable_2: 'mean bayes-corrected (q=0.75) extremity of replies'
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_stability_against_variation_in_weight_paired_ttest_bayes"
|
||
|
dataset: "data_order0"
|
||
|
variable_1: 'extremity'
|
||
|
variable_2: 'mean extremity of replies'
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_robustness_paired_ttest_order1"
|
||
|
dataset: "data_order1"
|
||
|
variable_1: 'bayes-corrected (q=0.25) extremity'
|
||
|
variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_paired_ttest_extremity_politics"
|
||
|
dataset: "data_politics"
|
||
|
variable_1: 'bayes-corrected (q=0.25) extremity'
|
||
|
variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_paired_ttest_extremity_foreign_affairs"
|
||
|
dataset: "data_foreign_affairs"
|
||
|
variable_1: 'bayes-corrected (q=0.25) extremity'
|
||
|
variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_paired_ttest_extremity_science"
|
||
|
dataset: "data_science"
|
||
|
variable_1: 'bayes-corrected (q=0.25) extremity'
|
||
|
variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_paired_ttest_extremity_economy"
|
||
|
dataset: "data_economy"
|
||
|
variable_1: 'bayes-corrected (q=0.25) extremity'
|
||
|
variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_paired_ttest_extremity_miscellaneous"
|
||
|
dataset: "data_miscellaneous"
|
||
|
variable_1: 'bayes-corrected (q=0.25) extremity'
|
||
|
variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_paired_ttest_extremity_culture"
|
||
|
dataset: "data_culture"
|
||
|
variable_1: 'bayes-corrected (q=0.25) extremity'
|
||
|
variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_paired_ttest_extremity_sports"
|
||
|
dataset: "data_sports"
|
||
|
variable_1: 'bayes-corrected (q=0.25) extremity'
|
||
|
variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_paired_ttest_extremity_mobility"
|
||
|
dataset: "data_mobility"
|
||
|
variable_1: 'bayes-corrected (q=0.25) extremity'
|
||
|
variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_paired_ttest_extremity_internet"
|
||
|
dataset: "data_internet"
|
||
|
variable_1: 'bayes-corrected (q=0.25) extremity'
|
||
|
variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
|
||
|
|
||
|
- !paired_ttest
|
||
|
name: "Evidence_polarization_paired_ttest_extremity_health"
|
||
|
dataset: "data_health"
|
||
|
variable_1: 'bayes-corrected (q=0.25) extremity'
|
||
|
variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
|
||
|
|
||
|
visualization:
|
||
|
- !hexbinplot
|
||
|
name: "Fig_2a"
|
||
|
dataset: "data_order0"
|
||
|
variable_x_axis: 'bayes-corrected (q=0.25) valence'
|
||
|
variable_y_axis: 'number O(n+1)-replies'
|
||
|
y_axis_maximum: 40
|
||
|
trendline: True
|
||
|
logarithmic_hex_scaling: True
|
||
|
|
||
|
- !forestplot
|
||
|
name: "Fig_2b"
|
||
|
regression_model_names:
|
||
|
- "Evidence_uncongenialty_section_politics"
|
||
|
- "Evidence_uncongenialty_section_foreign_affairs"
|
||
|
- "Evidence_uncongenialty_section_science"
|
||
|
- "Evidence_uncongenialty_section_economy"
|
||
|
- "Evidence_uncongenialty_section_miscellaneous"
|
||
|
- "Evidence_uncongenialty_section_culture"
|
||
|
- "Evidence_uncongenialty_section_sports"
|
||
|
- "Evidence_uncongenialty_section_mobility"
|
||
|
- "Evidence_uncongenialty_section_internet"
|
||
|
- "Evidence_uncongenialty_section_health"
|
||
|
regression_model_labels:
|
||
|
- "Politics"
|
||
|
- "Foreign Affairs"
|
||
|
- "Science"
|
||
|
- "Economy"
|
||
|
- "Miscellaneous"
|
||
|
- "Culture"
|
||
|
- "Sports"
|
||
|
- "Mobility"
|
||
|
- "Internet"
|
||
|
- "Health"
|
||
|
coefficient_names:
|
||
|
- "bayes-corrected (q=0.25) valence"
|
||
|
- "totalvotes"
|
||
|
x_axis_minimum: -0.6
|
||
|
dotsize: 2
|
||
|
x_axis_label: "Standardized coefficient (95% Confidence Interval)"
|
||
|
|
||
|
- !heatmap
|
||
|
name: "Fig_2c"
|
||
|
dataset: "data_order0_with_minimum_one_vote"
|
||
|
axis_variables:
|
||
|
- 'upvotes'
|
||
|
- 'downvotes'
|
||
|
heat_variable: 'number O(n+1)-replies'
|
||
|
axis_maxima:
|
||
|
- 20
|
||
|
- 20
|
||
|
axis_minima:
|
||
|
- 0
|
||
|
- 0
|
||
|
logarithmic_heat_scaling: 'false'
|
||
|
|
||
|
- !densityplot
|
||
|
name: 'Fig_3a'
|
||
|
dataset: "data_order0"
|
||
|
variable_x_axis: 'mean bayes-corrected (q=0.25) valence of replies'
|
||
|
variable_y_axis: 'bayes-corrected (q=0.25) valence'
|
||
|
data_breakpoints:
|
||
|
- 0
|
||
|
|
||
|
- !forestplot
|
||
|
name: "Fig_3b"
|
||
|
regression_model_names:
|
||
|
- "Evidence_antagonism_section_politics"
|
||
|
- "Evidence_antagonism_section_foreign_affairs"
|
||
|
- "Evidence_antagonism_section_science"
|
||
|
- "Evidence_antagonism_section_economy"
|
||
|
- "Evidence_antagonism_section_miscellaneous"
|
||
|
- "Evidence_antagonism_section_culture"
|
||
|
- "Evidence_antagonism_section_sports"
|
||
|
- "Evidence_antagonism_section_mobility"
|
||
|
- "Evidence_antagonism_section_internet"
|
||
|
- "Evidence_antagonism_section_health"
|
||
|
regression_model_labels:
|
||
|
- "Politics"
|
||
|
- "Foreign Affairs"
|
||
|
- "Science"
|
||
|
- "Economy"
|
||
|
- "Miscellaneous"
|
||
|
- "Culture"
|
||
|
- "Sports"
|
||
|
- "Mobility"
|
||
|
- "Internet"
|
||
|
- "Health"
|
||
|
coefficient_names:
|
||
|
- 'mean bayes-corrected (q=0.25) valence of replies'
|
||
|
x_axis_minimum: -0.1
|
||
|
dotsize: 2
|
||
|
x_axis_label: "Standardized coefficient (95% Confidence Interval)"
|
||
|
|
||
|
- !violinplot
|
||
|
name: "Fig_4a"
|
||
|
dataset: "data_order0"
|
||
|
variable_x_axis: 'bayes-corrected (q=0.25) extremity'
|
||
|
variable_y_axis: 'mean bayes-corrected (q=0.25) extremity of replies'
|
||
|
x_axis_label: ''
|
||
|
y_axis_label: 'Extremity value'
|
||
|
title: ''
|
||
|
|
||
|
- !forestplot_paired_ttest
|
||
|
name: "Fig_4b"
|
||
|
paired_ttest_names:
|
||
|
- "Evidence_polarization_paired_ttest_extremity_politics"
|
||
|
- "Evidence_polarization_paired_ttest_extremity_affairs"
|
||
|
- "Evidence_polarization_paired_ttest_extremity_science"
|
||
|
- "Evidence_polarization_paired_ttest_extremity_economy"
|
||
|
- "Evidence_polarization_paired_ttest_extremity_miscellaneous"
|
||
|
- "Evidence_polarization_paired_ttest_extremity_culture"
|
||
|
- "Evidence_polarization_paired_ttest_extremity_sports"
|
||
|
- "Evidence_polarization_paired_ttest_extremity_mobility"
|
||
|
- "Evidence_polarization_paired_ttest_extremity_internet"
|
||
|
- "Evidence_polarization_paired_ttest_extremity_health"
|
||
|
paired_ttest_labels:
|
||
|
- "Politics"
|
||
|
- "Foreign Affairs"
|
||
|
- "Science"
|
||
|
- "Economy"
|
||
|
- "Miscellaneous"
|
||
|
- "Culture"
|
||
|
- "Sports"
|
||
|
- "Mobility"
|
||
|
- "Internet"
|
||
|
- "Health"
|
||
|
x_axis_minimum: -0.06
|
||
|
dotsize: 2
|
||
|
x_axis_label: "Mean difference bayes-corrected (q=0.25) extremity (95% Confidence Interval)"
|
||
|
|
||
|
- !histogram
|
||
|
name: 'Extended_Fig_1'
|
||
|
dataset: "data"
|
||
|
variable: 'totalvotes'
|
||
|
x_axis_label: 'Number of total votes'
|
||
|
y_axis_label: 'Number of comments'
|
||
|
x_axis_logarithmic_scaling: false
|
||
|
y_axis_logarithmic_scaling: true
|
||
|
title: ''
|
||
|
...
|