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model assumption 0.001454918
user models 0.001451488
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generative model 0.001447865
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tive model 0.001409315
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joint model 0.001405525
model updates 0.001395246
model φvi 0.001379616
spatial model 0.001378296
hood model 0.001378296
such models 0.0013435270000000002
language technology 0.001342541
twitter data 0.0013363770000000001
language variations 0.001315395
graphic language 0.001314104
model 0.00118798
user content 0.001156753
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language 0.00112335
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data streams 0.001105352
data collection 0.001105225
twitter users 0.001101977
twitter user 0.0011004510000000001
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media data 0.001089474
binary word 0.001087172
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different accuracy 0.001076025
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text classification 0.0010621939999999998
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data heterogeneity 0.001053296
data policies 0.001053296
approach users 0.001049865
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batch models 0.0010462940000000001
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user attributes 0.001034617
different ways 0.00103374
word ngram 0.001030317
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test time 0.00101958
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different notions 0.001013723
different frequencies 0.001007998
different tradeoff 0.001007998
other methods 9.902460000000002E-4
users tweets 9.876429999999999E-4
user tweets 9.86117E-4
user attribute 9.54072E-4
users com 9.340379999999999E-4
user tweet 9.28222E-4
classification performance 9.21669E-4
feature representation 9.21635E-4
twitter social 9.193770000000001E-4
dynamic user 9.146849999999999E-4
user selection 9.04228E-4
users author 9.02788E-4
tweets figure 9.01007E-4
static user 8.9963E-4
republican users 8.94855E-4
user labels 8.945439999999999E-4
user stream 8.937509999999999E-4
latent user 8.93451E-4
user neighbor 8.82662E-4
neighbor user 8.82662E-4
graph twitter 8.819920000000001E-4
user baseline 8.78635E-4
baseline user 8.78635E-4
modeling user 8.772949999999999E-4
sample user 8.731780000000001E-4
democratic users 8.67015E-4
user name 8.620909999999999E-4
user gzlr 8.61576E-4
prediction time 8.60713E-4
user names 8.57187E-4
random twitter 8.552970000000001E-4
social graph 8.54789E-4
random set 8.54407E-4
relevant user 8.51448E-4
streaming user 8.50835E-4
user gen 8.50556E-4
