A Frame of Mind: Using Statistical Models for Detection of Framing and Agenda Setting Campaigns

By Oren Tsur, Dan Calacci, David M. J. Lazer. 2015

113 citations recorded by the source corpus.

Framing is a sophisticated form of discourse in which the speaker tries to induce a cognitive bias through consistent linkage between a topic and a specific context (frame). We build on political science and communication theory and use probabilistic topic models combined with time series regression analysis (autoregressive distributed-lag models) to gain insights about the language dynamics in the political processes. Processing four years of public statements issued by members of the U.S. Congress, our results provide a glimpse into the complex dynamic processes of framing, attention shifts and agenda setting, commonly known as ‘spin’. We further provide new evidence for the divergence in party discipline in U.S. politics.

Subfields: General Social Sciences, Political Science and International Relations, Sociology and Political Science, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In General Social Sciences

1. Yulia Tsvetkov
2. Dan Jurafsky
3. J. Radford
4. Noah A. Smith
5. Julia Mendelsohn
6. Joelle Pineau
7. Diyi Yang
8. Dirk Hovy

In Political Science and International Relations

1. Yulia Tsvetkov
2. Noah A. Smith
3. Dan Jurafsky
4. J. Radford
5. Julia Mendelsohn
6. David M. J. Lazer

In Sociology and Political Science

1. Yulia Tsvetkov
2. Diyi Yang
3. Noah A. Smith
4. J. Radford
5. Dan Jurafsky
6. Dirk Hovy
7. Julia Mendelsohn
8. Joelle Pineau

In Natural Language Processing

1. Noah A. Smith
2. Yulia Tsvetkov
3. Mohit Bansal
4. Diyi Yang
5. Dirk Hovy
6. Yejin Choi
7. Dan Jurafsky
8. Jason Aaron Edward Weston

Citations in the corpus

• Ethical Challenges in Data-Driven Dialogue Systems (2017, background): “These datasets are difficult to hand-filter and may include underlying biases (Henry and Tator 2002) or even subtle agenda setting campaigns (Tsur, Calacci, and Lazer 2015).”
• The Social Impact of Natural Language Processing (2016, background): “This influence stretches into politics and everyday competition, for example for turn-taking (Laskowski, 2010; Bracewell and Tomlin-son, 2012; Prabhakaran and Rambow, 2013; Prab-592 hakaran et al., 2014; Tsur et al., 2015; Khouzami et al., 2015, inter alia), .”
• Automatically Neutralizing Subjective Bias in Text (2019, methodology): “Several studies develop methods for highlighting subjective or persuasive frames in a text (Rashkin et al. 2017; Tsur, Calacci, and Lazer 2015), or detecting biased sentences (Hube and Fetahu 2018; Morstatter et al. 2018; Yang et al. 2017; Hube and Fetahu 2019) of which the most similar to ours is…”
• The Media Frames Corpus: Annotations of Frames Across Issues (2015, background): “More recently, there have also been a handful of papers on the computational analysis of framing (Nguyen et al., 2015; Tsur et al., 2015; Baumer et al., 2015).”
• A Framework for the Computational Linguistic Analysis of Dehumanization (2020, methodology): “, 2015) and unsupervised methods, such as topic modeling and lexicon induction (Tsur et al., 2015; Field et al., 2018; Demszky et al., 2019).”
• Data ex Machina: Introduction to Big Data (2017, methodology): “For example, new models of political partisanship are being generated by Federal Election Commission data, Twitter, press releases, and floor speeches (Barberá 2015, Bonica 2014, Gentzkow et al. 2016, Tsur et al. 2015).”