Andreas Niekler
Biographic Data
| ID | 4422326 |
|---|---|
| NAME | Andreas Niekler |
| GIVEN NAMES | Andreas |
| FAMILY NAME | Niekler |
| SIGNATURE | NIEKLER A |
| AFFILIATIONS | Leipzig University |
| ORCID | 0000-0002-3036-3318 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 66 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Big Data Discourses| Cultural Motifs of Big Data in User-Generated Content: A Semiautomated Analysis of 10 Years of Discourse
This article examines the sensemaking around big data in user-generated content on Reddit, Facebook, and Twitter/X. Big data is not only a technology but also an issue of public concern. However, given that the term “big data” has been around for more than a decade, little is known about how the cultural motifs used to make sense of it have changed over time or how public discussions reverberate with—or dispute—elite framings in news and high-pro…
Text as data for evaluation: Natural language processing and large language models to generate novel insights from unstructured text data
Policy formulation and implementation generate large volumes of text. However, since reading all relevant sources is often impossible, evaluators must navigate the complexities of selecting the appropriate technology to efficiently extract meaningful information from growing amounts of unstructured text. Text mining blends interpretative and statistical methods to generate novel insights, potentially contributing to evidence-based policy-making. …
Conceptual Forays: A Corpus-based Study of “Theory” in Digital Humanities Journals
The status of theory in the Digital Humanities (DH) has been the subject of much debate. As a result, we find different theory narratives competing and entangled with each other. If at all, these narratives can only be grasped and examined from a somewhat detached perspective. Here, we attempt to investigate these elusive narratives by means of a conceptual history approach. In doing so, we define different theory dimensions, ranging from specifi…
Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology
Latent Dirichlet allocation (LDA) topic models are increasingly being used in communication research. Yet, questions regarding reliability and validity of the approach have received little attention thus far. In applying LDA to textual data, researchers need to tackle at least four major challenges that affect these criteria: (a) appropriate pre-processing of the text collection; (b) adequate selection of model parameters, including the number of…
Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology
Latent Dirichlet allocation (LDA) topic models are increasingly being used in communication research. Yet, questions regarding reliability and validity of the approach have received little attention thus far. In applying LDA to textual data, researchers need to tackle at least four major challenges that affect these criteria: (a) appropriate pre-processing of the text collection; (b) adequate selection of model parameters, including the number of…
Text as data for evaluation: Natural language processing and large language models to generate novel insights from unstructured text data
Policy formulation and implementation generate large volumes of text. However, since reading all relevant sources is often impossible, evaluators must navigate the complexities of selecting the appropriate technology to efficiently extract meaningful information from growing amounts of unstructured text. Text mining blends interpretative and statistical methods to generate novel insights, potentially contributing to evidence-based policy-making. …
Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology
Latent Dirichlet allocation (LDA) topic models are increasingly being used in communication research. Yet, questions regarding reliability and validity of the approach have received little attention thus far. In applying LDA to textual data, researchers need to tackle at least four major challenges that affect these criteria: (a) appropriate pre-processing of the text collection; (b) adequate selection of model parameters, including the number of…
Conceptual Forays: A Corpus-based Study of “Theory” in Digital Humanities Journals
The status of theory in the Digital Humanities (DH) has been the subject of much debate. As a result, we find different theory narratives competing and entangled with each other. If at all, these narratives can only be grasped and examined from a somewhat detached perspective. Here, we attempt to investigate these elusive narratives by means of a conceptual history approach. In doing so, we define different theory dimensions, ranging from specifi…
Text as data for evaluation: Natural language processing and large language models to generate novel insights from unstructured text data
Policy formulation and implementation generate large volumes of text. However, since reading all relevant sources is often impossible, evaluators must navigate the complexities of selecting the appropriate technology to efficiently extract meaningful information from growing amounts of unstructured text. Text mining blends interpretative and statistical methods to generate novel insights, potentially contributing to evidence-based policy-making. …
Big Data Discourses| Cultural Motifs of Big Data in User-Generated Content: A Semiautomated Analysis of 10 Years of Discourse
This article examines the sensemaking around big data in user-generated content on Reddit, Facebook, and Twitter/X. Big data is not only a technology but also an issue of public concern. However, given that the term “big data” has been around for more than a decade, little is known about how the cultural motifs used to make sense of it have changed over time or how public discussions reverberate with—or dispute—elite framings in news and high-pro…
Computational and Text Analysis Methods (3 works) · Computer Science (3 works) · Artificial Intelligence (2 works) · Big data (2 works) · Natural language processing (2 works) · Advanced Text Analysis Techniques (1 works) · Annotation (1 works) · Big Data and Digital Economy (1 works) · Big Data Technologies and Applications (1 works) · Data mining (1 works)