@article {bib_2,
	title = {Identifying Issue Frames in Text},
	journal = {PLoS ONE},
	volume = {8},
	year = {2013},
	pages = {e69185},
	abstract = {Framing, the effect of context on cognitive processes, is a prominent topic of research in psychology and public opinion research. Research on framing has traditionally relied on controlled experiments and manually annotated document collections. In this paper we present a method that allows for quantifying the relative strengths of competing linguistic frames based on corpus analysis. This method requires little human intervention and can therefore be efficiently applied to large bodies of text. We demonstrate its effectiveness by tracking changes in the framing of terror over time and comparing the framing of abortion by Democrats and Republicans in the U.S.},
	doi = {10.1371/journal.pone.0069185},
	url = {http://dx.plos.org/10.1371/journal.pone.0069185},
	author = {Eyal Sagi and Daniel Diermeier and Stefan Kaufmann}
}
@article {49,
	title = {A quantitative approach to framing in political speech},
	year = {2012},
	type = {Talk},
	address = {Oxford, UK},
	url = {http://www.ncrm.ac.uk/TandE/video/RMF2012/filmed.php?id=24bc879},
	author = {Eyal Sagi and Daniel Diermeier and Stefan Kaufmann}
}
@article {50,
	title = {Using large corpora to explore the framing of concepts},
	year = {2012},
	type = {Talk},
	address = {Helsinki, Finland},
	abstract = {Psychologists and Social Scientists have long observed that the way in which a question or problem is presented to people can impact their attitudes and decisions. Framing is a widely discussed instance of this phenomenon: The choice of words and metaphors in talking about a given issue can affect hearers{\textquoteright} interpretations and biases, making some actions or strategies appear more plausible than others (1). The framing of issues is thus of great practical importance to those with an interest in steering the course of decision processes or justifying actions to a wider audience. For instance, different ways of dealing with drug abuse can become salient depending on whether the problem is presented as one of social policy or of law enforcement (2).
Consequently, the framing of issues is of interest to decision makers and is a prominent topic of research in Political Science, Sociology, Economics, Psychology and related fields. Researchers interested in these questions tend to rely on controlled experiments and manually annotated document collections. The recent explosion in the amount of textual data available electronically provides an opportunity for investigating framing on a large scale and tracking how it influences, and is influenced by, decisions made by governments and businesses. However, analyzing this massive amount of data requires tools to facilitate a fast and efficient analysis of the available data sets. To meet this demand, researchers have turned to machine-learning methods from computational linguistics for applications in topic analysis and opinion classification.
Most of these methods rely in some way on word co-occurrence patterns. A prominent example is Latent Semantic Analysis (LSA) (3), which has been applied to a wide range of tasks, including word sense discrimination, text summarization, and identifying semantic change (4-6). Here we present an LSA-based approach designed to observe and quantify variation in the framing of concepts across time or speaker/author populations. We illustrate this methodology using two examples of framing in political debates in the US senate: the rise and time-course of the framing of terror as a military struggle following the events of September 11th, 2001, and the different framings of abortion by democrats and republicans.

References
1. Tversky, A. \& Kahneman, D. (1981) The Framing of decisions and the psychology of choice. Science 211 (4481): 453{\textendash}458.
2. Mark, E. (2003) War on Drugs: Legislation in the 108th Congress and Related Development, Congressional Research Service Report IB10113 ( http://opencrs.com/document/IB10113/ ).
3. Landauer, T. K., \& Dumais, S. T. (1997). A solution to Plato{\textquoteright}s problem: The Latent Semantic Analysis theory of the acquisition, induction, and representation of knowledge. Psychological Review, 104, 211-240.
4. Sch{\"u}tze, H. (1998) Automatic word sense discrimination. Computational Linguistics 24(1):97-124.
5. Marcu, D (2003) Automatic Abstracting, In Darke, M. A. (ed.) Encyclopedia of Library and Information, 245-256.
6. Sagi, E., Kaufmann, S., \& Clark, B. (2009). Semantic Density Analysis: Comparing Word Meaning across Time and Phonetic Space. In Basili R., and Pennacchiotti M. (Eds.), Proceedings of the EACL 2009 Workshop on GEMS: Geometrical Models of Natural Language Semantics. Athens, Greece.},
	author = {Eyal Sagi and Daniel Diermeier and Stefan Kaufmann}
}
@article {51,
	title = {Quantitative and Qualitative approaches for tracing changes to the framing of issues in political speeches},
	year = {2011},
	address = {University of Chicago, Chicago, IL},
	author = {Eyal Sagi and Stefan Kaufmann and Daniel Diermeier}
}
@inbook {german2011role,
	title = {The role of speaker beliefs in determining accent placement},
	booktitle = {Language, games, and evolution},
	year = {2011},
	pages = {92{\textendash}116},
	publisher = {Springer},
	organization = {Springer},
	address = {New York, NY},
	author = {James German and Eyal Sagi and Stefan Kaufmann and Brady Clark},
	editor = {A. Bentz and C. Ebert and G. Jager and R. van Rooij}
}
@inbook {42,
	title = {Tracing semantic change with Latent Semantic Analysis},
	booktitle = {Current Methods in Historical Semantics},
	year = {2011},
	pages = {161-183},
	publisher = {Mouton de Gruyter},
	organization = {Mouton de Gruyter},
	address = {Berlin, Germany},
	author = {Eyal Sagi and Stefan Kaufmann and Brady Clark},
	editor = {Kathryn Allan and Justyna A Robinson}
}
@article {37,
	title = {Tracking Changes in Word Meaning and Use},
	year = {2010},
	type = {Talk},
	address = {Stuttgart, Germany},
	author = {Eyal Sagi and Stefan Kaufmann and Brady Clark and Daniel Diermeier}
}
@conference {21,
	title = {Culture in the Mirror of Language: A Latent Semantic Analysis Approach to Culture},
	booktitle = {Proceedings of the 31st Annual Conference of the Cognitive Science Society},
	year = {2009},
	pages = {637-642},
	address = {Amsterdam, The Netherlands},
	abstract = {In the social sciences, culture is often explored via the use of knowledgeable informants and direct observation. In this paper we present a novel approach for cultural investigation that focuses on the statistical analysis of texts. We use Latent Semantic Analysis to generate a semantic space representing one or more cultures based on a corpus of texts. By comparing the vector representation of texts within this corpus it is possible to gain insights into cultural change. We demonstrate this method by exploring the divergence of British and American societies following the Revolutionary War. Possible uses of this method for exploratory and experimental research are discussed.},
	author = {Eyal Sagi and Stefan Kaufmann and Brady Clark},
	editor = {Niels Taatgen and Hedderik van Rijn and Lambert Schomaker and John Nerbonne}
}
@conference {20,
	title = {Semantic Density Analysis: Comparing Word Meaning across Time and Phonetic Space},
	booktitle = {Proceedings of the EACL 2009 Workshop on GEMS: Geometrical Models of Natural Language Semantics},
	year = {2009},
	pages = {104-111},
	address = {Athens, Greece},
	abstract = {This paper presents a new statistical method for detecting and tracking changes in word meaning, based on Latent Semantic Analysis. By comparing the density of semantic vector clusters this method allows researchers to make statistical inferences on questions such as whether the meaning of a word changed across time or if a phonetic cluster is associated with a specific meaning. Possible applications of this method are then illustrated in tracing the semantic change of "dog","do", and "deer" in early English and examining and comparing phonaesthemes.},
	author = {Eyal Sagi and Stefan Kaufmann and Brady Clark},
	editor = {Roberto Basili and Marco Pennacchiotti}
}
@article {41,
	title = {Tracing semantic change with Latent Semantic Analysis},
	year = {2009},
	address = {Northwestern Institute on Complex Systems (NICO) weekly seminar series, Evanston, IL},
	author = {Eyal Sagi and Stefan Kaufmann and Brady Clark}
}
@article {24,
	title = {Tracing semantic change with Latent Semantic Analysis},
	year = {2009},
	type = {Talk},
	address = {San Francisco, CA},
	author = {Eyal Sagi and Stefan Kaufmann and Brady Clark}
}
@article {26,
	title = {Tracing semantic change with Latent Semantic Analysis},
	year = {2008},
	type = {Talk},
	address = {Munich, Germany},
	author = {Eyal Sagi and Stefan Kaufmann and Brady Clark}
}
@article {30,
	title = {The Effect of the Speaker{\textquoteright}s Motivation on the Interpretation of Logical Connectives},
	year = {2007},
	type = {Poster},
	address = {Nashville, TN},
	author = {James German and Eyal Sagi and Stefan Kaufmann and Brady Clark and Min-Joo Kim}
}
@article {16,
	title = {The role of hearer{\textquoteright}s beliefs in the interpretation of logical connectives},
	year = {2006},
	type = {Talk},
	address = {Berlin, Germany},
	author = {James German and Eyal Sagi and Brady Clark and Min-Joo Kim and Stefan Kaufmann}
}
