项目作者: christokita

项目描述 :
Examining how information cascades driven by polarized media can cause "echo chambers" to emerge in social networks. Agent-based model and empirical data collection from Twitter.
高级语言: Python
项目地址: git://github.com/christokita/information-cascades.git
创建时间: 2017-12-15T15:16:33Z
项目社区:https://github.com/christokita/information-cascades

开源协议:

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user_ideology_bynewssource_1648291972151.pdf
user_vs_follower_ideology_1648291972218.pdf
user_vs_follower_ideology_TEST_1648291972286.pdf
user_vs_follower_ideology_bynewsource_1648291972345.pdf
estimated_user_news_diet_1648291972378.pdf
news_ideology_histogram_1648291972393.pdf
raw_user_news_diet_1648291972428.pdf
estimated_gamma_1648291972517.pdf
model_assort_with_newsestimantes_1648291972531.pdf
model_tiebreaks_with_newsestimantes_1648291972565.pdf
posteriordiff_relativefreq_infoeco_1648291972580.pdf
posteriorest_relativefreq_infoeco_1648291972643.pdf
raw_relativefreq_infoeco_1648291972658.pdf
raw_relativefreq_newssource_1648291972678.pdf
raw_relativenumber_infoeco_1648291972735.pdf
raw_relativenumber_newssource_1648291972770.pdf
relativefreq_infoeco_1648291972849.pdf
relativefreq_newssource_1648291972863.pdf
relativeideology_newssource_1648291972877.pdf
relativenumber_infoeco_1648291972893.pdf
relativenumber_newssource_1648291972915.pdf
unadjusted_freq_infoeco_1648291972948.pdf
unadjusted_freq_newssource_1648291972970.pdf
unfollow_rate_1648291973004.pdf
follower_change_1648291946041.pdf
friend_change_1648291946074.pdf
assortativity_by_tie_formation_1648291960677.pdf
centrality_by_tie_formation_1648291960726.pdf
degree_by_tie_formation_1648291960740.pdf
localassort_by_tie_formation_1648291960754.pdf
assortativity_by_tie_formation_1648291960801.pdf
behaviorrates_by_tie_formation_1648291960815.pdf
behaviorrates_1648291960895.pdf
behaviorrates_highthresh_1648291960938.pdf
behaviorrates_lowthresh_1648291960973.pdf
cascadeactivity_1648291961028.pdf
cascadebias_1648291961073.pdf
cascadesize_1648291961140.pdf
precision_1648291961154.pdf
sensitivity_1648291961168.pdf
specificity_1648291961207.pdf
threshold-activity_1648291961427.pdf
threshold-falsenegative_1648291962246.pdf
threshold-falsepositive_1648291963083.pdf
threshold-precision_1648291963833.pdf
threshold-sensitivity_1648291964634.pdf
threshold-specificity_1648291965470.pdf
threshold-truenegative_1648291966267.pdf
threshold-truepositive_1648291967194.pdf
assortativity_comparison_1648291967946.pdf
assortativity_threshold_1648291967960.pdf
assortativity_type_1648291967975.pdf
assortchange_type_1648291967988.pdf
threshold-centrality_1648291969948.pdf
threshold-degree_1648291969964.pdf
threshold-individualnetworkmetrics_1648291970157.pdf
threshold-localassort_1648291971293.pdf
threshold-networkmetriccoeffs_1648291971308.pdf
change_in_neighbor_similarity_1648291971347.pdf
change_in_neighbor_threshold_1648291971361.pdf
gamma1_change_lowthresh_neighbors_1648291971413.pdf
gamma1_neighbor_thresholds_1648291971502.pdf
local_threshold_assort_1648291971523.pdf
tie_breaks_1648291971545.pdf
tie_breaksandadds_1648291971589.pdf
tie_change_summary_1648291971645.pdf
tie_netchange_1648291971684.pdf
compare_estimation_methods_1648291971782.pdf
follower_conservative_bynewssource_1648291971839.pdf
follower_ideology_1648291971922.pdf
follower_same_ideology_1648291971963.pdf
ideology_distribution_by_method_1648291971996.pdf
method_vs_newssource_1648291972070.pdf
sampledusers_ideology_bynewssource_1648291972137.pdf