Computer-aided content analysis of digitally enabled movements

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
Youth Movement
3. Describe and walk through coding procedure

Alexander Hanna, Wisconsin (@alexhanna)

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


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)


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)


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.


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

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

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


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

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?”

Expectations
1. Increased offline coordination directly before
mobilization dates
2. Increased reporting and press on
mobilization dates
...but not clear what will go afterward.

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


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

Validation
k-fold cross validation as a common method of
validation

Source: http://www.imtech.res.
in/raghava/gpsr/Evaluation_Bioinformatics_Methods.htm


Validation results
Following Hopkins and King (2010), split dataset in half,
using one half to estimate other


Automated
analysis


Automated
analysis
Mostly no mobilization


Automated
analysis
Peaks in offline
coordination before
days of mobilization


Automated
analysis
Peaks in offline
coordination before
days of mobilization
Language matters in
requests for
information

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


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

Supplementary
material

Methods of automated textual
analysis


Supervised methods
“Supervised”
because humans do
manual coding
Categories are
defined a priori to
coding
Analogous to
regression


Unsupervised methods
“Unsupervised”
because machine
defines categories
from statistical cooccurrence
Analogous to factor
analysis


Multilingual stemming
Consists, consisting, consistent -> consist
‫اﻟﻌرﺑﯾﺔ، ﻋرﺑﯾﺔ -< ﻋرﺑﻲ‬
al 3rabiyya, 3rabiyya -> 3rabi


Dataset features


Word search
analysis
Coordination terms
low throughout
Media as peaking on
days of action


Computer-aided content analysis of digitally enabled movements

Recomendados

Recomendados

Más contenido relacionado

Similar a Computer-aided content analysis of digitally enabled movements

Similar a Computer-aided content analysis of digitally enabled movements (20)

Último

Último (20)

Computer-aided content analysis of digitally enabled movements