Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology
2018531 citationsDaniel Maier, Annie Waldherr et al.Communication Methods and Measuresprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Andreas Niekler
Since
Specialization
Citations
This map shows the geographic impact of Andreas Niekler's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Andreas Niekler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Niekler more than expected).
This network shows the impact of papers produced by Andreas Niekler. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Andreas Niekler. The network helps show where Andreas Niekler may publish in the future.
Co-authorship network of co-authors of Andreas Niekler
This figure shows the co-authorship network connecting the top 25 collaborators of Andreas Niekler.
A scholar is included among the top collaborators of Andreas Niekler based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Andreas Niekler. Andreas Niekler is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Niekler, Andreas, et al.. (2020). Language Model CNN-driven Similarity Matching and Classification for HTML-embedded Product Data.. elib (German Aerospace Center).2 indexed citations
Maier, Daniel, Annie Waldherr, Peter Miltner, et al.. (2018). Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology. Communication Methods and Measures. 12(2-3). 93–118.531 indexed citations breakdown →
12.
Wiedemann, Gregor & Andreas Niekler. (2017). Hands-On: A Five Day Text Mining Course for Humanists and Social Scientists in R.. 57–65.9 indexed citations
Haenig, Christian, et al.. (2014). PACE Corpus: a multilingual corpus of Polarity-annotated textual data from the domains Automotive and CEllphone. Language Resources and Evaluation. 2219–2224.1 indexed citations
15.
Niekler, Andreas, Gregor Wiedemann, & Gerhard Heyer. (2014). Leipzig Corpus Miner - A Text Mining Infrastructure for Qualitative Data Analysis. HAL (Le Centre pour la Communication Scientifique Directe).1 indexed citations
Wiedemann, Gregor, Matthias Lemke, & Andreas Niekler. (2013). Postdemokratie und Neoliberalismus: zur Nutzung neoliberaler Argumentationen in der Bundesrepublik Deutschland 1949-2011; ein Werkstattbericht. Social Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences). 4(1). 99–115.5 indexed citations
18.
Rohrdantz, Christian, Andreas Niekler, Annette Hautli-Janisz, Miriam Butt, & Daniel A. Keim. (2012). Lexical Semantics and Distribution of Suffixes - A Visual Analysis. KOPS (University of Konstanz). 7–15.7 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.