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UID/CPO/04627/2013, SFRH/BPD/78955/2011. ; This paper reviews the logic of attempts to automate the processes involved in computer-assisted text analysis in the social sciences. Bayesian estimation methods in spatial analysis of variations in positions of political parties over time and Latent Dirichlet Allocation from the developing field of latent topic analysis are compared with the analysis of structures of word co-occurrences in the tradition of content analysis, using Procrustean individual differences scaling. Each depends in practice on concentrating attention on a limited number of word tokens regarded as meaningful while most are disregarded as inessential. By applying apparently competing strategies to the same set of party contributions to the 1997 budget debate in the Italian parliament, they can beshown to be complementary in character and should be applied as such in comparing material of this kind. ; publishersversion ; published

This paper reviews the logic of attempts to automate the processes involved in computer-assisted text analysis in the social sciences. Bayesian estimation methods in spatial analysis of variations in positions of political parties over time and Latent Dirichlet Allocation from the developing field of latent topic analysis are compared with the analysis of structures of word co-occurrences in the tradition of content analysis, using Procrustean individual differences scaling. Each depends in practice on concentrating attention on a limited number of word tokens regarded as meaningful while most are disregarded as inessential. By applying apparently competing strategies to the same set of party contributions to the 1997 budget debate in the Italian parliament, they can be shown to be complementary in character and should be applied as such in comparing material of this kind.

In political science, research using computer-assisted text analysis techniques has exploded in the last fifteen years. This scholarship spans work studying political ideology,1 congres-sional speech,2 representational style,3 American foreign policy,4 climate change attitudes,5 media,6 Islamic clerics,7 and treaty making,8 to name but a few. As these examples illustrate, com-puter-assisted text analysis—a prime example of mixed-meth-ods research—allows gaining new insights from long-familiar political texts, like parliamentary debates, and altogether en-ables the analysis to new forms of political communication, such as those happening on social media.

Events as elections, significant changes in laws, but also extreme weather conditions, may affect societal values and, consequently, public opinion. Accordingly, a central assumption for public opinion surveys in that respondents behavior is influenced by significant events. It is therefore necessary to consider the impact of potential events when designing a survey and, whenever possible, to control for these. To support the documentation of such social events, the authors have developed a procedure to identify events using computer-assisted text analysis. Event words are selected and grouped by means of exploratory factor analysis based on a comparison of a large text corpus that forms the reference for a smaller text corpus consisting of media items on significant events. As a result, the factors represent significant events during a specific time period. (author's abstract)

"This report presents an account of methods of research in computer-assisted text analysis in
the social sciences. Rather than to provide a comprehensive enumeration of all computer-assisted
text analysis investigations either directly or indirectly related to the social sciences using a
quantitative and computer-assisted methodology as their text analytical tool, the aim of this report is to describe the current methodological standpoint of computer-assisted text analysis in the social sciences. This report provides, thus, a description and a discussion of the operations carried out in computer-assisted text analysis investigations. The report examines both past and well-established as well as some of the current approaches in the field and describes the techniques and the procedures involved. By this means, a first attempt is made toward cataloguing the kinds of supplementary information as well as computational support which are further required to expand the suitability and applicability of the method for the variety of text analysis goals." (author's abstract)

Recent advances in research tools for the systematic analysis of textual data are enabling exciting new research throughout the social sciences. For comparative politics, scholars who are often interested in non-English and possibly multilingual textual datasets, these advances may be difficult to access. This article discusses practical issues that arise in the processing, management, translation, and analysis of textual data with a particular focus on how procedures differ across languages. These procedures are combined in two applied examples of automated text analysis using the recently introduced Structural Topic Model. We also show how the model can be used to analyze data that have been translated into a single language via machine translation tools. All the methods we describe here are implemented in open-source software packages available from the authors.

The article focuses on the online storytelling concerning the Xylella fastidiosa outbreak in the Apulia region (Italy), as represented by a collection of User-generated content retrieved from Facebook, YouTube, and Reddit over a time span of 6 years (>16k comments). We examine the episode as a revelatory case of framing mechanisms that, in many technoscientific conflicts, enable different epistemologies to compete on an equal basis: «mainstream» scientific knowledge, and «alternative» contents (i.e. «non-orthodox» science, local and traditional knowledge). We use Computer-Assisted Text Analysis (CATA) to investigate popular themes and their semantic vocabulary. We find that discourses on Xylella fastidiosa are strongly polarized and structured around two conflicts: «expertise vs. politics» on one hand, and «scientific vs alternative» solutions on the other. Then, we identify three main knowledge production strategies and introduce a typology of «non-mainstream» methods and cures, highlighting the formal traits and argumentation strategies that may have made them more credible than knowledge provided by «official» science. We nevertheless call for more research that may find a recursiveness in such framing mechanisms in the online representation of other technoscientific conflicts.

"This paper provides an overview of the current state of the art in computer-assisted content analysis (CACA). First, background, history and a model of CACA will be given, the dichotomy of qualitative versus quantitative is addressed, and a new understanding, the 'marker view' leading to a more general Text Analysis is introduced. Subsequent chapters provide a definition of terms and cover issues of size of scoring units, and the development of computerized coding to replace well established manual rating systems. The paper concludes with the description of a recently developed computer-assisted text analysis methodology to describe psychotherapeutic processes." (author's abstract)

"Two developments in computational text analysis may change the way qualitative data analysis in social sciences is performed: 1. the availability of digital text worth to investigate is growing rapidly, and 2. the improvement of algorithmic information extraction approaches, also called text mining, allows for further bridging the gap between qualitative and quantitative text analysis. The key factor hereby is the inclusion of context into computational linguistic models which extends conventional computational content analysis towards the extraction of meaning. To clarify methodological differences of various computer-assisted text analysis approaches the article suggests a typology from the perspective of a qualitative researcher. This typology shows compatibilities between manual qualitative data analysis methods and computational, rather quantitative approaches for large scale mixed method text analysis designs." (author's abstract)

Purpose – Since the early 1980s, western governments are assumed to have been either moving toward post-bureaucratic models or transforming into so-called neo-Weberian bureaucracies. As different public-sector (reform) models imply different ideal typical personality traits for civil servants, the purpose of this paper is to ask the question to what extent personality requirements that governments demand from their employees have evolved over time in line with these models. Design/methodology/approach – The authors analyzed the use of big-five traits in a sample of 21,003 job advertisements for local government jobs published between 1980 and 2017, applying tools for computer-assisted text analysis. Findings – Using multilevel regression analyses, the authors conclude that, over time, there is a significant increase in the use of personality descriptors related to all big-five factors. Research limitations/implications – The authors postulate that governments nowadays are actively looking for the "renaissance bureaucrat" in line with the neo-Weberian bureaucracy paradigm. The authors end with a discussion of both positive and negative consequences of this development. Originality/value – First, the authors explicitly link personality, public administration, and public management using the Abridged Big-Five-Dimensional Circumflex model of personality. Second, by linking observed trends in civil servant personality requirements to larger theories of public-sector reform models, the authors narrow the gap between public administration theories and practice. Third, the software tools that the authors use to digitalize and analyze a large number of documents (the job ads) are new to the discipline of public administration. The research can therefore serve as a guideline for scholars who want to use software tools to study large amounts of unstructured, qualitative data

A review is offered of Concepts and Tools of Computer Assisted Policy Analysis, (Bossel, H. [Ed], Birkhauser Verlag, 1977), a three-volume collection of seventeen papers based on the premises that: (1) system models of societal systems 'neglect the cognitive/normative components of the real processes which dominate decision making & action,' & (2) current simulation methodology 'is not geared to the communication & analysis modes & requirements of those in applied policy analysis.' Attention is focused on: (A) foundations for policy analysis, (B) basic concepts for computer-assisted analysis, (C) tools for the analysis of object systems, & (D) the analysis of cognition in policy evaluation. This work, which is largely the product of the Instit fur Systemtechnik and Innovationsforschung (Instit for System Technology & Innovation Research) at Karlsruhe represents one of the most important contributions to the theory & practice of policy evaluation made in recent years. M. Cain.

Content analysis provides a useful and multifaceted, methodological framework
for Twitter analysis. CAQDAS tools support the structuring of textual data by
enabling categorising and coding. Depending on the research objective, it may
be appropriate to choose a mixed-methods approach that combines quantitative
and qualitative elements of analysis and plays out their respective advantages
to the greatest possible extent while minimising their shortcomings.

In this chapter, we will discuss CAQDAS speech act analysis of tweets as an example
of software-assisted content analysis. We start with some elementary thoughts
on the challenges of the collection and evaluation of Twitter data before we
give a brief description of the potentials and limitations of using the software
QDA Miner (as one typical example for possible analysis programmes). Our
focus will lie on analytical features that can be particularly helpful in speech
act analysis of tweets.