When Scale Meets Depth: Integrating Natural Language Processing and Textual Analysis for Studying Digital Corpora

Shahin, Saif

doi:10.1080/19312458.2015.1118447

Cited by 15 publications

(14 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…13-14, our emphasis). This is in keeping with a healthy tradition of hybrid methods encompassing both computational and manual elements and drawing strength from both (e.g., Muddiman et al, 2019;Shahin, 2016).…”

Section: Framing Theory and Computational Analysissupporting

confidence: 56%

Computational Identification of Media Frames: Strengths, Weaknesses, and Opportunities

Nicholls

Culpepper

2020

Political Communication

View full text Add to dashboard Cite

With the availability of large volumes of electronic communications data and the increasing sophistication of computational techniques, the development of automated approaches for different kinds of framing analysis is an important goal of researchers. There is as yet no standard method for the 'unsupervised' inductive identification of frames based upon the content of articles. Three groups of core approaches underlie a wide range of work in this area, and we compare three techniques based on these approaches against each other and against manual human analysts. The three techniques are a k-means clustering algorithm together with a sophisticated natural language processing (NLP)-based feature selection process; evolutionary factor analysis (EFA), a factor analysis approach; and the structural topic model (STM). We use two datasets-one very focused and one extremely broad-as examples of the kind of frame analysis problems readers may wish to attempt. Even in a highly targeted dataset, we find some distance between the frames generated by computational analysis and those manually produced by our human analysts. The details of each method have a substantial impact on frame quality and interpretation. We find that the STM approach is the most effective for our narrow-scope dataset, but that it returns definite topics, and not frames, when working on our very broad dataset. We also show that we can get results emphasizing different parts of the framing problem by combining parts of different methods as a multi-stage process, rather than viewing available methods as simple plug-and-play models.

show abstract

Section: Framing Theory and Computational Analysissupporting

confidence: 56%

Computational Identification of Media Frames: Strengths, Weaknesses, and Opportunities

Nicholls

Culpepper

2020

Political Communication

View full text Add to dashboard Cite

show abstract

“…As the field evolves, the limits of these Big Data methodologies are also being recognized and addressed -often by combining multiple techniques that offset each other's shortcomings (Lewis, Zamith, & Hermida, 2013;Shahin, 2016aShahin, , 2016b). …”

Section: Research With Big Datamentioning

confidence: 99%

“…Such research would be motivated by a spirit of social justice -as opposed to advancing the interests of governments and businesses. Equally importantly, it would pay heed to the epistemological, methodological, and ethical/normative concerns that have been raised vis-à-vis conventional Big Data research (see also Shahin, 2016a).…”

Section: Critical Research With Big Datamentioning

confidence: 99%

“…In my own research (Shahin, 2016a), I have used a methodological approach that combines natural language processing with Python and interpretive analysis to study large-volume textual data in a theoretically grounded and contextually sensitive manner -illustrating it with two case studies. The first case study examines the Inaugural Address Database, a collection of the inaugural addresses of all U.S. presidents from George Washington to Barack Obama.…”

Section: Critical Research With Big Datamentioning

confidence: 99%

“…Anderson, 2008) is slowly giving way to uneasiness about the social effects of enormous datasets and the algorithms used to compile and analyze them (boyd & Crawford, 2012;Crawford, Miltner, & Gray, 2014;Mahrt & Scharkow, 2013;Manovich, 2012;Shahin, 2016a). Reports of malpractices by major Big Data-enabled enterprises such as Facebook and Google that compromise user privacy (Dwyer, 2011;Rubenstein & Good, 2012), along with Edward Snowden's revelation that the U.S. government was running surveillance programs on a global scale in collusion with technology companies (Bauman et al, 2014;Lyon, 2014), have made it plain that Big Data is not the panacea for all human problems that it is sometimes made out to be.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Critical Axiology for Big Data Studies

Shahin¹

2016

Pacla

Self Cite

View full text Add to dashboard Cite

Big Data is having a huge impact on journalism and communication studies. At the same time, it has raised a plethora of social concerns ranging from mass surveillance to the legitimization of prejudices such as racism. This article develops an agenda for critical Big Data research. It discusses what the purpose of such research should be, what pitfalls it should guard against, and the possibility of adapting Big Data methods to conduct empirical research from a critical standpoint. Such a research program will not only enable critical scholarship to meaningfully challenge Big Data as a hegemonic tool, but will also make it possible for scholars to draw upon Big Data resources to address a range of social issues in previously impossible ways. The article calls for methodological innovation in combining emerging Big Data techniques with critical/qualitative methods of research, such as ethnography and discourse analysis, in ways that allow them to complement each other. KeywordsBig data; technology; social media; critical research; surveillance (Source: Unesco Thesaurus). Una axiología crítica para los estudios de Big Data ResumenLos datos masivos (Big Data) han tenido un gran impacto en el periodismo y los estudios de comunicación, a la vez que han generado un gran número de preocupaciones sociales que van desde la vigilancia masiva hasta la legitimación de prejuicios, como el racismo. En este artículo, se desarrolla una agenda para la investigación crítica de Big Data y se discute cuál debería ser el propósito de dicha investigación, de qué obstáculos protegerse y la posibilidad de adaptar los métodos de Big Data para llevar a cabo la investigación empírica desde un punto de vista crítico. Dicho programa de investigación no solo permitirá que la erudición crítica desafíe significativamente a Big Data como una herramienta hegemónica, sino que también permitirá que los académicos usen los recursos de Big Data para abordar una serie de problemas sociales de formas previamente imposibles. El artículo llama a la innovación metodológica para combinar las técnicas emergentes de Big Data y los métodos críticos y cualitativos de investigación, como la etnografía y el análisis del discurso, de tal manera que se puedan complementar. Palabras claveBig Data; tecnología; medios de comunicación sociales; investigación crí-tica; vigilancia (Fuente: Tesauro de la Unesco). 974A Critical Axiology for Big Data Studies -Saif Shahin Uma axiologia crítica para os estudos de Big Data ResumoOs megadados (Big Data) têm tido um grande impacto sobre o jornalismo e os estudos de comunicação, e têm gerado um grande número de preocupações sociais, desde a vigilância em massa até a legitimação de preconceitos, como o racismo. Neste artigo se desenvolve uma agenda para a investigação crítica do Big Data e se discute qual deveria ser o propósito dessa investigação, de quais obstáculos se protegerem e a possibilidade de adaptar os métodos de Big Data para realizar a pesquisa empírica a partir de um ponto de vista crítico. Esse programa de...

show abstract

Content Analysis, Automatic

Scharkow¹

2017

The International Encyclopedia of Communication Research Methods

View full text Add to dashboard Cite

Automatic content analysis (ACA) is a technique for coding messages with the help of computer algorithms. Unlike computer-aided content analysis, ACA is defined as any method in which the actual coding decision, that is, assigning codes to documents or single textual or audiovisual elements, does not require human judgment and therefore is performed automatically. Since ACA relies on the computing capabilities of machines rather than human coders, it can be applied to very large documents. Moreover, automatic coding is highly reliable in that any analysis can be reproduced exactly given the same material and software and all errors are deterministic, that is, stemming from misspecification or program errors. Historical developmentThe history and development of ACA can be understood through three central themes: (i) the concept of content analysis and its computational implementation, (ii) the development of software for automatic data processing and analysis, and (iii) the provision and analysis of digital or machine-readable documents. The first phase of development of computer-assisted and automatic content analysis in the late 1950s was mainly characterized by experiments with the computer. For this, social scientists had to first learn to program the computer, and in most cases, this involved the support of the science departments. The first few experiments were almost exclusively limited to producing text statistics, that is, counting words, a technique that had been applied in many disciplines such as political science or literature since the 1920s (Holsti, 1969;Stone, Dunphy, Smith, & Ogilvie, 1966). Conceptually, these simple automatic analyses fell well behind the advancements made in conventional content analysis by Lasswell, Lerner, and de Sola Pool (1952) or Osgood (1959). However, early content analysts were increasingly troubled by the costs of manual coding and had high expectations from automatic approaches, the development of which was viewed as central to the success of the method (Stone, 1997). At this time, computer-assisted content analysis was a high-risk research domain, plagued with numerous problems. On the one hand, primitive computing hardware and software allowed only small amounts of text to be analyzed with a few variables (Iker & Harway, 1965). On the other hand, because of the dearth of machine-readable documents, all units of analysis had to be digitized in a complex and error-prone process on punch cards. Thus, in the early 1960s, ACA was neither cost-effective-half an

show abstract

When Scale Meets Depth: Integrating Natural Language Processing and Textual Analysis for Studying Digital Corpora

Cited by 15 publications

References 37 publications

Computational Identification of Media Frames: Strengths, Weaknesses, and Opportunities

Computational Identification of Media Frames: Strengths, Weaknesses, and Opportunities

A Critical Axiology for Big Data Studies

Content Analysis, Automatic

Contact Info

Product

Resources

About