260 citations · 976 across the 18 of their papers we have counts for
21 papers
Orphan Articles: The Dark Matter of Wikipedia
Akhil Arora, Robert West, Martin Gerlach
With 60M articles in more than 300 language versions, Wikipedia is the largest platform for open and freely accessible knowledge. While the available content has been growing conti…
Curious Rhythms: Temporal Regularities of Wikipedia Consumption
Tiziano Piccardi, Martin Gerlach, Robert West
Wikipedia, in its role as the world's largest encyclopedia, serves a broad range of information needs. Although previous studies have noted that Wikipedia users' information needs…
Going Down the Rabbit Hole: Characterizing the Long Tail of Wikipedia Reading Sessions
Tiziano Piccardi, Martin Gerlach, Robert West
"Wiki rabbit holes" are informally defined as navigation paths followed by Wikipedia readers that lead them to long explorations, sometimes involving unexpected articles. Although…
Wikipedia Reader Navigation: When Synthetic Data Is Enough
Akhil Arora, Martin Gerlach, Tiziano Piccardi +2
Every day millions of people read Wikipedia. When navigating the vast space of available topics using hyperlinks, readers describe trajectories on the article network. Understandin…
A Large-Scale Characterization of How Readers Browse Wikipedia
Tiziano Piccardi, Martin Gerlach, Akhil Arora +1
Despite the importance and pervasiveness of Wikipedia as one of the largest platforms for open knowledge, surprisingly little is known about how people navigate its content when se…
Multilayer Networks for Text Analysis with Multiple Data Types
Charles C. Hyland, Yuanming Tao, Lamiae Azizi +3
We are interested in the widespread problem of clustering documents and finding topics in large collections of written documents in the presence of metadata and hyperlinks. To tack…
A Multilingual Entity Linking System for Wikipedia with a Machine-in-the-Loop Approach
Martin Gerlach, Marshall Miller, Rita Ho +2
Hyperlinks constitute the backbone of the Web; they enable user navigation, information discovery, content ranking, and many other crucial services on the Internet. In particular,…
Language-agnostic Topic Classification for Wikipedia
Isaac Johnson, Martin Gerlach, Diego Sáez-Trumper
A major challenge for many analyses of Wikipedia dynamics -- e.g., imbalances in content quality, geographic differences in what content is popular, what types of articles attract…
A Taxonomy of Knowledge Gaps for Wikimedia Projects (Second Draft)
Miriam Redi, Martin Gerlach, Isaac Johnson +2
In January 2019, prompted by the Wikimedia Movement's 2030 strategic direction, the Research team at the Wikimedia Foundation identified the need to develop a knowledge gaps index…
Testing statistical laws in complex systems
Martin Gerlach, Eduardo G. Altmann
The availability of large datasets requires an improved view on statistical laws in complex systems, such as Zipf's law of word frequencies, the Gutenberg-Richter law of earthquake…
A new evaluation framework for topic modeling algorithms based on synthetic corpora
Hanyu Shi, Martin Gerlach, Isabel Diersen +2
Topic models are in widespread use in natural language processing and beyond. Here, we propose a new framework for the evaluation of probabilistic topic modeling algorithms based o…
A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics
Martin Gerlach, Francesc Font-Clos
The use of Project Gutenberg (PG) as a text corpus has been extremely popular in statistical analysis of language for more than 25 years. However, in contrast to other major lingui…
A network approach to topic models
Martin Gerlach, Tiago P. Peixoto, Eduardo G. Altmann
One of the main computational and scientific challenges in the modern age is to extract useful information from unstructured texts. Topic models are one popular machine-learning ap…
Using text analysis to quantify the similarity and evolution of scientific disciplines
Laercio Dias, Martin Gerlach, Joachim Scharloth +1
We use an information-theoretic measure of linguistic similarity to investigate the organization and evolution of scientific fields. An analysis of almost 20M papers from the past…
Generalized Entropies and the Similarity of Texts
Eduardo G. Altmann, Laercio Dias, Martin Gerlach
We show how generalized Gibbs-Shannon entropies can provide new insights on the statistical properties of texts. The universal distribution of word frequencies (Zipf's law) implies…
Is this scaling nonlinear?
J. C. Leitao, J. M. Miotto, M. Gerlach +1
One of the most celebrated findings in complex systems in the last decade is that different indexes y (e.g., patents) scale nonlinearly with the population~x of the cities in which…
Similarity of symbol frequency distributions with heavy tails
Martin Gerlach, Francesc Font-Clos, Eduardo G. Altmann
Quantifying the similarity between symbolic sequences is a traditional problem in Information Theory which requires comparing the frequencies of symbols in different sequences. In…
Statistical laws in linguistics
Eduardo G. Altmann, Martin Gerlach
Zipf's law is just one out of many universal laws proposed to describe statistical regularities in language. Here we review and critically discuss how these laws can be statistical…
Extracting information from S-curves of language change
Fakhteh Ghanbarnejad, Martin Gerlach, Jose M. Miotto +1
It is well accepted that adoption of innovations are described by S-curves (slow start, accelerating period, and slow end). In this paper, we analyze how much information on the dy…
Scaling laws and fluctuations in the statistics of word frequencies
Martin Gerlach, Eduardo G. Altmann
In this paper we combine statistical analysis of large text databases and simple stochastic models to explain the appearance of scaling laws in the statistics of word frequencies.…
Stochastic model for the vocabulary growth in natural languages
Martin Gerlach, Eduardo G. Altmann
We propose a stochastic model for the number of different words in a given database which incorporates the dependence on the database size and historical changes. The main feature…