Network Analysis Literacy

Zweig, Katharina Anna

doi:10.1007/978-3-7091-0741-6

Cited by 49 publications

(38 citation statements)

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“…Adding regular expressions that capture typos and abbreviations (method 3) further improves the hit rate to 72.4% and also increases the κ-coefficient to 79%. Given that many methods in social network analysis are quite sensitive to false-negative and false-positive edges [12,Chapter 5], the hit rate itself is not a helpful measure but it shows that least, with method 3 − 6, enough statements are actually assigned a receiver. Methods 3 to 6 show very close κ coefficients that are nearly indistinguishable; however, method 5 has a marginally higher quality.…”

Section: A Resultsmentioning

confidence: 99%

Constructing social networks from semi-structured chat-log data

Tavassoli¹,

Moessner²,

Zweig³

2014

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)

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Chat-log data is a little used resource for analyzing human communication in social networks. Some statements in this data do not include the intended username of a receiver or any variant of it, and thus are termed "misaddressed statements". Constructing social networks from such a semi-structured data and subsequent analyzing require a reliable process to make sure that the social network representation is as truthful as possible. Due to the large size of data, human assignment of statements to receivers is prohibitive. In this paper, we present and evaluate different methods to reliably predict a receiver for these misaddressed statements. We use a set of prediction rules which follow human communication behavior in a group chat and we show their success in constructing social networks.

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Section: A Resultsmentioning

confidence: 99%

Constructing social networks from semi-structured chat-log data

Tavassoli¹,

Moessner²,

Zweig³

2014

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)

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“…In addition, there are data processing basics, such as the ability to read in all tables of FlyClockbase and produce a report of all inconsistencies and errors that require human attention. The arrival of big data has brought substantial experience with questions of data hygiene (G oldston 2008; H owe et al 2008; K rishnamurthy et al 2011; G itelman 2013; M c C allum 2013; S chutt and O’N eil 2013; M ahmood 2016; Z weig 2016). Most of this expertise is also essential for correctly and efficiently handling data in VBIRs.…”

Section: Discussionmentioning

confidence: 99%

“…2008; K rishnamurthy et al . 2011; G itelman 2013; M c C allum 2013; S chutt and O’N eil 2013; M ahmood 2016; Z weig 2016). Most of this expertise is also essential for correctly and efficiently handling data in VBIR s. For all features like those above and all error types detected, a solution only needs to be implemented once for simultaneously improving the reliability of all VBIR s.…”

Section: Towards a Compiler For Advancing Flyclockbase And Biologymentioning

confidence: 99%

FlyClockbase: Importance of Biological Model Curation for Analyzing Variability in the Circadian Clock of Drosophila melanogaster by Integrating Time Series from 25 Years of Research

Scheuer

Hanlon

Dresel

et al. 2017

Preprint

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General Article SummarySubstantial biological model curation was essential for identifying differences in peak 23 time variance of the clock-proteins 'PERIOD' and 'TIMELESS', which probably stem 24 from differences in phosphorylation-network complexity. 25 We repeatedly encountered systemic limitations of contemporary data analysis 26 strategies in our work on circadian clocks. Thus, we used it as an opportunity for 27 composing a panoramic view of the broader challenges in biological model curation, 28 which are likely to increase as biologists aim to integrate all existing expertise in order to 29 address diverse grand challenges. We developed and tested a trans-disciplinary 30 research workflow, which enables biologists and compiler-architects to define biology-31 friendly compilers for efficiently constructing and maintaining Versioned Biological 32 Information Resources (VBIRs). We report insights gleaned from our practical clock 33 research that are essential for defining a VBIRs infrastructure, which improves the 34 efficiency of biological model curation to the point where it can be democratized. . CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It ...

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“…Day 13 (Analysis of social networks, time activity of publishing pages, identification of harmful sources of information in networks with the use of artificial neural networks, machine learning), M. Kaya, Ö Erdogan 14 (Memes on social networks studying and clustering, similarities between texts, emerging events identifying, forecast), K.A. Zweig 15 (Network analytical activities, methods of presenting data in the form of complex networks, models of random graphs, centrality indices and their use in network analysis, the humanitarian aspect of the analysis of social networks, etc). A large number of works are currently devoted to the in-depth analysis of information from social networks, among which the monographs by M. Russell 16,17 (Information extraction from various social networks, research of application program interfaces of various networks, analysis of text files, determination of text similarity, classification, pattern recognition, neural networks in the analysis of social networks).…”

Section: Introductionmentioning

confidence: 99%

A System for Analysis of Big Data from Social Media

Lande¹,

Subach²,

Puchkov³

2020

ISIJ

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The article presents the basic principles of building and using a monitoring and analysis system of social media on cybersecurity, based on the concepts of Big Data, Data/Text Mining, Information Extraction, Complex Networks. The authors substantiate information technologies for creating a system of content monitoring, selection of relevant information from social networks, implementation of search engines for their refinement by users, saving queries as RSS feeds, and maintaining personal databases in client applications. The described OSINT system is based on collection of information from open sources, its analysis, preparation and timely delivery of the final product to the customer in order to solve certain intelligence tasks. Hence, the system is the result of a systematic collection, processing and analysis of the necessary publicly available information. It is based on the application of methods and tools of information retrieval, data analysis and aggregation of information flows, and is used for social media content monitoring as a component of decision support systems for information and cybersecurity.

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Network Analysis Literacy

Cited by 49 publications

References 0 publications

Constructing social networks from semi-structured chat-log data

Constructing social networks from semi-structured chat-log data

FlyClockbase: Importance of Biological Model Curation for Analyzing Variability in the Circadian Clock of Drosophila melanogaster by Integrating Time Series from 25 Years of Research

A System for Analysis of Big Data from Social Media

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