Protest Event Analysis
Developing a Semiautomated NLP Approach
Bibliographic Data
| ID | 3759524 |
|---|---|
| Authors | Jasmine Lorenzini (0000-0002-4667-4209, University of Geneva, Geneva, Switzerland, corresponding author), Hanspeter Kriesi (0000-0003-4229-8960, European University Institute, San Domenico di Fiesole, Italy), Peter Makarov (University of Zurich, Zurich, Switzerland), Bruno Wüest (0000-0002-1216-0832, Sotomo, Zurich, Switzerland) |
| Year | 2022 |
| Volume | 66 |
| Issue | 5 |
| Pages | 555-577 |
| Publication date | 2022-05-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Behavioral Scientist (JOURNAL) |
| Journal identifiers | ISSN: 0002-7642 • E-ISSN: 1552-3381 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/00027642211021650 |
| OpenAlex | W3167108461 |
| Language | EN |
| Citations received | 14 |
| References cited | 24 |
Protest event analysis is a key method to study social movements, allowing to systematically analyze protest events over time and space. However, the manual coding of protest events is time-consuming and resource intensive. Recently, advances in automated approaches offer opportunities to code multiple sources and create large data sets that span many countries and years. However, too often the procedures used are not discussed in details and, therefore, researchers have a limited capacity to assess the validity and reliability of the data. In addition, many researchers highlighted biases associated with the study of protest events that are reported in the news. In this study, we ask how social scientists can build on electronic news databases and computational tools to create reliable PEA data that cover a large number of countries over a long period of time. We provide a detailed description our semiautomated approach and we offer an extensive discussion of potential biases associated with the study of protest events identified in international news sources
Coding (social sciences) · Computer security · Data science · Event (particle physics) · Information retrieval · Key (lock) · Natural language processing · Reliability (semiconductor) · Resource (disambiguation) · Sentiment analysis · Social science · Sociology · Computational and Text Analysis Methods · Computer Science · Media Influence and Politics · Social Media and Politics
Demonstracje równoczesne i problem pomiaru „siły liczb”
Analityczna socjologia historyczna. Perspektywy dyscypliny
Convention, Protest, or Violence? Estimating the Influence of Repertoires of Contention over Tactical Choice
Discovering Representations of Democracy in Big Data
Introducing the Turkey Protest, Repression, and Pro-Government Rally Dataset (TPRPGRD)
One foot in parliament, one on the streets
Collecting protest event data using natural language processing models
Political-RAG
The Polarizing Effect of Anti-Immigrant Violence on Radical Right Sympathies in Germany
Movement parties in Europe
Papea
Far-right contentious politics in times of crisis
Salient Indigenous Acts of Resistance in Canada, 2010–2020
Varieties of trade union protest
Protesting Culture and Economics in Western Europe
Protest Event Analysis and Its Offspring
Machine learning in automated text categorization
New Social Movements In Western Europe
An End to “Patience”?
Systemic Determinants of International News Coverage
From bias to coverage
Precedents, Progress, and Prospects in Political Event Data
What Should We Do about Source Selection in Event Data? Challenges, Progress, and Possible Solutions
National protest agenda and the dimensionality of party politics
Political Processes and Local Newspaper Coverage of Protest Events
Text as Data
Where Do We Stand with Newspaper Data
Local Receptivity Climates and the Dynamics of Media Attention to Protest
A Closer Look at Reporting Bias in Conflict Event Data
Casm
Great Methods Reveal Their Own Limitations
A Progressive Supervised-learning Approach to Generating Rich Civil Strife Data
All the Movements Fit to Print
Making the News
The Use of Newspaper Data in the Study of Collective Action
| Unique citing works | 14 |
|---|---|
| Citations per year | 2,8 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 14 |