Semantic Network Analysis in Social Science …vanatteveldt.com/p/skn2015_atteveldt_semanticnetworks.pdfBig Data Semantic Netwrko Analysis Gaza Wra News Dynamics Semantic Network Analysis

Big Data Semantic Network Analysis Gaza War News Dynamics

Semantic Network Analysis

in Social Science Research

Wouter van Atteveldthttp://vanatteveldt.com

December 3, 2015

Semantic Network Analysis Wouter van Atteveldt | http://vanatteveldt.com


Big Data and Social Analytics

Are daters racist? (self-report)




But are they?




And did it get better?




Outline

1 Big Data and Social Analytics

2 Semantic Network Analysis

3 Application: Gaza War

4 Application: News Dynamics




Daily trail of information

� Smartphone location

� RFID

� License plates

� Twitter, Facebook, . . .

� Shopping (online & o�ine)

� Reviews (book, hotel)

� Dating

� Electronic Patient File

� Insurance claims





� Big Data

� Data Science

� Computational Social Science

� Social Physics




Opportunities: Research paradigms

� Experiments:

� A/B testing vs. traditional controlled� Behaviour vs. self-report� milllions vs. 30 per cell

� Observational studies:

� Behaviour vs. self-report� Millions vs. hundreds� Real time vs. waves



The Facebook Voting Experiment

Example: Facebook voting experiment

� A 61-million-person experiment in social in�uence andpolitical mobilization

� Bond et al (2012), Nature

� Do online messages increase voting behaviour?

� E�ect of social pressure and contagion




Previous studies

� Experiments and '�eld' experiments

� Small-N

� Weak results

� Voting is 'contagious'




Facebook experiment

� Informational vs social vs control

� N=61 million (!)

� E�ect on vote intention, information search, behaviour













Conclusion

� Social message promotes voting

� Voting behaviour contagious

� E�ect small, but margins are small too

� p>.001 even with 61M respondents



Social Analytics: Conclusions




Social Analytics

� Massive amounts of data

� Opportunities

� Bridging experimental gap� Unbiased, real-time, sensitive measures� Bring social back in social science

� Threats

� Privacy� Reproducibility� Technical complexity




Privacy risks




Outline








Social and Semantic Network Analysis

� Big data is often (also) textual

� Semantic Network Analysis

� Extracted from text� Nodes are words/concepts, edges similarities/relations

� Social Network Analysis

� Nodes are people, edges connections(friendship, co-author, etc)

� Mostly extracted from structured data




Extracting Social/Semantic Networks from text

1 Identify relevant entities (nodes) in text

2 Determine whether there is a relation

� Co-occurrence in same document� Co-occurrence in a certain window

3 Determine nature of relation




Example 1: HPV discussions




Example 2: Enron email




Example 3: BP Crisis




Example 4: Stakeholders and themes





� Di�erent data than social networks

� Nodes can be people, but also concepts, actors, etc.� Connected nodes are "related" in the source texts

� Similar measures

� Centrality, connectedness, etc.� Edge/node level modelling

� Can be combined

� Semantic network can be integrated in social network� If people mail/chat, what do they say?



Gaza War: Extracting directed relations

Outline








Combining social and semantic analysis

� Text: who does what to whom?

� Yields "labeled" actor-actor network

� e.g. John loves Mary

� John � (loves) �> Mary




Grammatical analysis

� Co-occurence is undirected

� Word order not reliable

� (esp. outside English)

� → Grammatical analysis




Method

1 Syntactic Parsing

2 Source detection

3 Clause identi�cation

4 Extraction as tokens




Parsing




Source Detection (Dutch)




Clauses

� Who does what to whom?

� Semantic Role Labeling / verb subframe identi�cation

� Identifying verbs and agents is easy

� Di�erentiating between object and predicate is hard

� He shot a rocket at him� He shot him at dawn

� Clauses: subject/predicate tuples

� ("Old" meaning of predicate)




Clauses



Explorative results

Explorative results

� 2009 Gaza war

� US and Chinese news media

� LexisNexis, English language articles� US: 6.5k articles, Chinese: 2.2k� Total 800k clauses

� Simple dictionary for 'Israel'/'Hamas' and 'aggression'



Explorative results

Who does what? (According to who)



Explorative results

What is being done?

� Select words in predicates

� Compare Israeli subject vs Hamas subject

� chi-squared overrepresentation

� Occurrence and simple co-occurrence in predicate



Explorative results

(Overrepresented predicate words, subject Israel, US Media)



Explorative results

(Overrepresented predicate words, subject Hamas, US Media)



Explorative results

(Overrepresented pred. words, subject Israel, Chinese Media)



Explorative results

(Overrepresented pred. words, subject Hamas, Chinese Media)



Explorative results

Dyadic words

� Who does what to whom?

� Frequent words on dyadic edges

� actor in subject → actor in predicate

� EU, US, Israel and Palestine



Explorative results

Israel → Palestine



Explorative results

US → Israel



Explorative results

EU → Israel



News Dynamics: Media as nodes

Outline








Journalism and the Internet

� Increasing amount of news outlets

� Is news actually more diverse?

� Journalists copy sources, each other

� Especially press agency

� Who determines what you read?




Gatekeeping




Networked gatekeeping

� Audience reached directly or indirectly

� Network of in�uence from gatekeepers

� High betweenness: strong in�uence






In�uence of ANP

In�uence of ANP

� Semantic Network Analysis of newspaper dynamics

� Nodes: articles� Edges: copied / highly similar articles

� Who follows whom?



In�uence of ANP



In�uence of ANP



In�uence of ANP

Similarity between newspapers



In�uence of ANP

Similarity controlled for ANP



In�uence of ANP



Conclusions

Conclusions

� Digital text important source of information

� Who is connected to whom?� Content of relations

� Semantic Network Analysis

� Complement to Social Network Analysis� Importance of tooling

� Opens up new research questions

� Journalism: objectivity and dynamics� Many other �elds: social psychology, sociology,economics, organization science, . . .



Conclusions

Batteries included

� This presentation:http://vanatteveldt.com/skn2015

� http://vanatteveldt.com/

� http://vanatteveldt.com/learningr

� https://rawgit.com/vanatteveldt/learningr/

master/clauses.html

� http://github.com/amcat/amcat-r

� http://github.com/kasperwelbers/corpus-tools

� http://github.com/vanatteveldt/xtas


http://vanatteveldt.com/skn2015

http://vanatteveldt.com/

http://vanatteveldt.com/learningr

https://rawgit.com/vanatteveldt/learningr/master/clauses.html

https://rawgit.com/vanatteveldt/learningr/master/clauses.html

http://github.com/amcat/amcat-r

http://github.com/kasperwelbers/corpus-tools

http://github.com/vanatteveldt/xtas

Documents

Semantic Network Analysis in Social Science …vanatteveldt.com/p/skn2015_atteveldt_semanticnetworks.pdfBig Data Semantic Netwrko Analysis Gaza Wra News Dynamics Semantic Network Analysis