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Computer-aided content
analysis of digitallyenabled movements
Alexander Hanna
University of Wisconsin-Madison
@alexhanna
Agenda
1. Focus on methods of prior digitally-mediated
movement research
2. Outline application to specific case, April 6
...
The rise of digitally-enabled
movements
Digitally-enabled movement - Movements that
have incorporated some aspect of onlin...
Digitally-enabled movement activity
Coordinating people for other (usually offline) activities (emobilization in Earl and ...
Previous types of analysis
Case study (e.g. Gurak 1997, Eaton 2010)
Network analysis (e.g. Garrido and Halavais
2003, Benn...
Problems with these methods
Need to focus on
content, but too much
data for manual
content analysis
Cost and time
prohibit...
A solution - computer-aided content
analysis
Computer-aided content analysis (also called
automated content analysis; text...
Case: April 6 Youth Movement
Facebook group
created as solidarity
action with Egyptian
workers
Proposed actions on
April 6...
Classification method of current
study
Classification of documents for a number of set
categories as a possible tool for s...
Supervised machine learning
“Supervised” means 1) categories known a
priori; 2) involves handcoding
“Training set” is hand...
Categories for classification
Offline coordination (e-mobilization)
- Example: “get to Tahrir Square”
Internet action (e.g...
Expectations
1. Increased offline coordination directly before
mobilization dates
2. Increased reporting and press on
mobi...
Analysis process
1. Data collection
2. Coding training set
3. Reliability testing of training set
4. Data preprocessing
5....
Data collection, coding,
preprocessing
Data collection
- Scraping of FB group page March-May 2008
- 64,197 messages, 3,841...
Validation
k-fold cross validation as a common method of
validation

Source: http://www.imtech.res.
in/raghava/gpsr/Evalua...
Validation results
Following Hopkins and King (2010), split dataset in half,
using one half to estimate other

Alexander H...
Automated
analysis

Alexander Hanna, Wisconsin (@alexhanna)
Automated
analysis
Mostly no mobilization

Alexander Hanna, Wisconsin (@alexhanna)
Automated
analysis
Mostly no mobilization
Peaks in offline
coordination before
days of mobilization

Alexander Hanna, Wisc...
Automated
analysis
Mostly no mobilization
Peaks in offline
coordination before
days of mobilization
Language matters in
re...
Discussion
Expectations
- Coordination increased before action
- ...but no other categories did
Possible avenues for error...
Conclusion
Rise of “big data” necessitates new methods, development
of “computational social science” (Lazer et al. 2010)
...
Supplementary
material
Alexander Hanna, Wisconsin (@alexhanna)
Methods of automated textual
analysis

Alexander Hanna, Wisconsin (@alexhanna)
Supervised methods
“Supervised”
because humans do
manual coding
Categories are
defined a priori to
coding
Analogous to
reg...
Unsupervised methods
“Unsupervised”
because machine
defines categories
from statistical cooccurrence
Analogous to factor
a...
Multilingual stemming
Consists, consisting, consistent -> consist
‫اﻟﻌرﺑﯾﺔ، ﻋرﺑﯾﺔ -< ﻋرﺑﻲ‬
al 3rabiyya, 3rabiyya -> 3rabi
...
Dataset features

Alexander Hanna, Wisconsin (@alexhanna)
Word search
analysis
Coordination terms
low throughout
Media as peaking on
days of action

Alexander Hanna, Wisconsin (@al...
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Computer-aided content analysis of digitally enabled movements

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Computer-aided content analysis of digitally enabled movements

  1. 1. Computer-aided content analysis of digitallyenabled movements Alexander Hanna University of Wisconsin-Madison @alexhanna
  2. 2. Agenda 1. Focus on methods of prior digitally-mediated movement research 2. Outline application to specific case, April 6 Youth Movement 3. Describe and walk through coding procedure Alexander Hanna, Wisconsin (@alexhanna)
  3. 3. The rise of digitally-enabled movements Digitally-enabled movement - Movements that have incorporated some aspect of online activity through information and communication technologies (ICTs) Movements leave traces which are both 1) records of events and 2) movement activity in and of themselves Alexander Hanna, Wisconsin (@alexhanna)
  4. 4. Digitally-enabled movement activity Coordinating people for other (usually offline) activities (emobilization in Earl and Kimport 2012) Online mobilizations (e-tactics, ibid) Issue discussion and development of discourses (analogous to “free spaces” [Evans and Boyte 1986; Polletta 1999] or counterpublics) Persuasion and micromobilization (Snow et al. 1986) Alexander Hanna, Wisconsin (@alexhanna)
  5. 5. Previous types of analysis Case study (e.g. Gurak 1997, Eaton 2010) Network analysis (e.g. Garrido and Halavais 2003, Bennett, Foot, Xenos 2011) Volume and group properties (e.g. Caren, Jowers, and Gaby 2012) Alexander Hanna, Wisconsin (@alexhanna)
  6. 6. Problems with these methods Need to focus on content, but too much data for manual content analysis Cost and time prohibitive to code by hand Too many Datas. Alexander Hanna, Wisconsin (@alexhanna)
  7. 7. A solution - computer-aided content analysis Computer-aided content analysis (also called automated content analysis; textual analysis) - Goal: extracting information out of text - Includes word search, statistical machine learning, language modeling Note: this supplements deep case knowledge, encourages both inductive and deductive approaches Alexander Hanna, Wisconsin (@alexhanna)
  8. 8. Case: April 6 Youth Movement Facebook group created as solidarity action with Egyptian workers Proposed actions on April 6, strike date, and May 4, Mubarak’ s birthday Alexander Hanna, Wisconsin (@alexhanna)
  9. 9. Classification method of current study Classification of documents for a number of set categories as a possible tool for study of digitally-enabled movements Supervised machine learning, reporting proportions of categories in a body of texts (Hopkins and King 2010) Categories derived from theory, interviews, and coding process Alexander Hanna, Wisconsin (@alexhanna)
  10. 10. Supervised machine learning “Supervised” means 1) categories known a priori; 2) involves handcoding “Training set” is handcoded “Test set” as uncoded, to be coded by algorithm Think of supervised machine learning like regression analysis Alexander Hanna, Wisconsin (@alexhanna)
  11. 11. Categories for classification Offline coordination (e-mobilization) - Example: “get to Tahrir Square” Internet action (e.g. “e-tactics) - Example: changing profile pictures Media and press - Example: links to BBC, al-Jazeera Reporting on events - Example: citizen journalism, pictures of events Request for information - Example: “What is happening right now in Tahrir?” Alexander Hanna, Wisconsin (@alexhanna)
  12. 12. Expectations 1. Increased offline coordination directly before mobilization dates 2. Increased reporting and press on mobilization dates ...but not clear what will go afterward. Alexander Hanna, Wisconsin (@alexhanna)
  13. 13. Analysis process 1. Data collection 2. Coding training set 3. Reliability testing of training set 4. Data preprocessing 5. Validation 6. Applying analysis across dataset Alexander Hanna, Wisconsin (@alexhanna)
  14. 14. Data collection, coding, preprocessing Data collection - Scraping of FB group page March-May 2008 - 64,197 messages, 3,841 unique users - Messages in Arabic, English, and “Franco” Human coding of “training set” - 638 messages, assessed intercoder reliability Data preprocessing - Stemming Generating different parameters per language - Focusing only on Arabic, Franco Alexander Hanna, Wisconsin (@alexhanna)
  15. 15. Validation k-fold cross validation as a common method of validation Source: http://www.imtech.res. in/raghava/gpsr/Evaluation_Bioinformatics_Methods.htm Alexander Hanna, Wisconsin (@alexhanna)
  16. 16. Validation results Following Hopkins and King (2010), split dataset in half, using one half to estimate other Alexander Hanna, Wisconsin (@alexhanna)
  17. 17. Automated analysis Alexander Hanna, Wisconsin (@alexhanna)
  18. 18. Automated analysis Mostly no mobilization Alexander Hanna, Wisconsin (@alexhanna)
  19. 19. Automated analysis Mostly no mobilization Peaks in offline coordination before days of mobilization Alexander Hanna, Wisconsin (@alexhanna)
  20. 20. Automated analysis Mostly no mobilization Peaks in offline coordination before days of mobilization Language matters in requests for information Alexander Hanna, Wisconsin (@alexhanna)
  21. 21. Discussion Expectations - Coordination increased before action - ...but no other categories did Possible avenues for error - Coder misclassification in the training set - Insufficient information in training set Alexander Hanna, Wisconsin (@alexhanna)
  22. 22. Conclusion Rise of “big data” necessitates new methods, development of “computational social science” (Lazer et al. 2010) Drawing on computer-aided methods for content analysis of digitally-enabled movement texts The process requires extensive prep, theory- and datainformed categories, sufficient case knowledge, validation Necessary to integrate these methods with existing quantitative and qualitative ones Alexander Hanna, Wisconsin (@alexhanna)
  23. 23. Supplementary material Alexander Hanna, Wisconsin (@alexhanna)
  24. 24. Methods of automated textual analysis Alexander Hanna, Wisconsin (@alexhanna)
  25. 25. Supervised methods “Supervised” because humans do manual coding Categories are defined a priori to coding Analogous to regression Alexander Hanna, Wisconsin (@alexhanna)
  26. 26. Unsupervised methods “Unsupervised” because machine defines categories from statistical cooccurrence Analogous to factor analysis Alexander Hanna, Wisconsin (@alexhanna)
  27. 27. Multilingual stemming Consists, consisting, consistent -> consist ‫اﻟﻌرﺑﯾﺔ، ﻋرﺑﯾﺔ -< ﻋرﺑﻲ‬ al 3rabiyya, 3rabiyya -> 3rabi Alexander Hanna, Wisconsin (@alexhanna)
  28. 28. Dataset features Alexander Hanna, Wisconsin (@alexhanna)
  29. 29. Word search analysis Coordination terms low throughout Media as peaking on days of action Alexander Hanna, Wisconsin (@alexhanna)

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