Bursty and Hierarchical Structure in Streams

By Jon M. Kleinberg. Data mining and knowledge discovery, 2002

2,529 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a38468d0c7bc403581740324f229b61f85c2a706

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis (2010, neutral): “We adopt the burst detection algorithm introduced in (Kleinberg, 2002).”
Social innovation research: An emerging area of innovation studies? (2016, methodology): “For our analysis of bursts, we applied the burst detection algorithm developed by Kleinberg (2003) and implemented in the Sci2 software program (Sci2-Team, 2009).”
Cultural intelligence, global mindset, and cross-cultural competencies: a systematic review using bibliometric methods (2020, methodology): “The algorithm then outputs a list of these words together with the beginning and end of the burst, as well as the burst strength (also called weight), to indicate the change in usage frequency (e.g., Guo et al., 2011; Kleinberg, 2003).”
Predicting the Political Alignment of Twitter Users (2011, background): “Similarly, burst-detection mechanisms could be employed to detect a rise in prominence of a specific candidate or issue, allowing campaigns to shape marketing and messaging efforts in response to emerging topics and trends [11], [41], [42].”
Parameter Free Bursty Events Detection in Text Streams (2005, methodology): “[9] shows how to extract bursty features from text streams based on modeling the text stream using an inﬁnite-state automaton, where bursts are modeled as state transitions.”
A Survey of Text Clustering Algorithms (2012, methodology): “In order to achieve this goal, the approach uses Kleinberg’s 2-state finite automaton model [57].”
On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking (2008, background): “Considering time information for the task of identifying and tracking topics in time-stamped text data is the focus of recent studies (e.g. [4, 7, 10, 11]).”
Tracking information epidemics in blogspace (2005, background): “It may be that restricting textual similarity to words representing a new or “bursty” topic, as in [13] [18], would enhance the predictive ability of textual data.”
Learning similarity functions for topic detection in online reputation monitoring (2014, methodology): “[30] apply Kleinberg’s probabilistic automata method [21] to blogs burst modeling and extracting structure from a text stream.”