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Overview of methods and approaches in Social Sciences
Published on Jul 27, 20171709 Views
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Chapter list
Doing Text Analytics for Digital Humanities and Social Sciences with CLARIN00:00
NLP for computational social science00:10
Traditional data sources in the social sciences - 100:19
Traditional data sources in the social sciences - 200:43
Traditional data sources in the social sciences - 300:49
Big social and cultural data - 101:18
Big social and cultural data - 201:44
Today: Focus on methodological challenges02:22
NLP for theory building and explanation03:01
Exploration vs prediction03:07
Opinions..04:17
Explanation vs. prediction05:20
NLP for theory building and explanation - 106:45
NLP for theory building and explanation - 207:11
Cultural fit in organisations07:12
Cultural fit in organisations II08:29
Cultural fit in organisations III09:53
Cultural fit in organisations IV11:00
NLP for theory building and explanation - 312:35
Topic modeling12:44
LDA example13:48
Topic modeling examples14:09
Grounded theory14:43
Topic modeling vs grounded theory I15:38
Topic modeling vs grounded theory II16:21
Topic modeling vs grounded theory III17:08
NLP for theory building and explanation - 418:31
Movember18:43
Scaling up the Social Identity Model of Collective Action - 119:10
Scaling up the Social Identity Model of Collective Action - 220:13
Scaling up the Social Identity Model of Collective Action - 320:24
Philip Bloom21:02
Automatic classification of Movember profiles21:18
Findings22:01
Alternative22:59
NLP for theory building and explanation - 523:51
Interpretable models24:05
Making the model more interpretable24:45
Extract an interpretable model25:44
Global vs. local explanation27:10
Interpreting neural networks for politeness prediction I - 128:03
Interpreting neural networks for politeness prediction I - 228:58
Local explanation: LIME I29:42
Local explanation: LIME II31:19
Local explanation: LIME III32:01
Interpretable models?32:38
Causality I33:09
Causality II33:56
Causality III34:15
Summary - 134:35
References - 135:07
Data bias35:14
Representativeness & bias35:33
Data source selection I36:23
Data source selection II38:22
Data source selection III39:26
Bias in Twitter: demographics40:01
Bias in Twitter: language I41:30
Bias in Twitter: language II42:20
Bias Twitter: sampling42:54
Google books I - 145:49
Google books I - 246:21
Google books II46:41
Google books III47:39
Google books IV48:03
Summary - 248:51
References - 249:28
Small vs. big data49:31
Making big data small again49:41
NLP for small data50:19
Supporting small data analysis50:50
References - 352:23
Ethical challenges52:26
Privacy I52:35
Privacy II53:19
Data representativeness54:06
POS taggers: age groups54:52
POS taggers: AAVE55:37
Language identification: AAE56:12
Perpetuation of bias: word embeddings I57:29
Perpetuation of bias: word embeddings II59:09
Finder gender stereotype analogies01:00:08
Detecting bias - 101:01:23
Detecting bias - 201:01:49
Perpetuation of bias in sentiment analysis01:02:16
Reinforcing stereotypes? gender in NLP I01:03:13
Reinforcing stereotypes? gender in NLP II01:04:01
Reinforcing stereotypes? gender in NLP III01:04:39
Reinforcing stereotypes? gender in NLP IV01:05:59
Reinforcing stereotypes? gender in NLP V01:07:00
References: critical look on gender in NLP01:07:53
Conclusion - 101:07:56
Conclusion - 201:08:15
Conclusion - 301:08:18
Questions?01:08:47