Ostinato: The Exploration-Automation Cycle of User-Centric, Process-Automated Data-Driven Visual Network Analytics

As an interdisciplinary field, data-driven journalism integrates the intellectual origins of investigative journalism, computer-assisted reporting, and the emerging paradigm of computational social science. Studies of news production have revealed, however, that news professionals are reinforcing existing power structures via an interpretive community, where homophily-evoked social interactions—even in the social media context—create echo chambers and discussion fragmentation. Is the representation of data-driven journalism in the electronic public sphere breaking boundaries among people from different domains or does it resemble the existing power structure? This study adopts a network analytics approach and constructs a representational network among actors who joined the public discussion of data-driven journalism in the Twittersphere—the co-retweeted network—such that two accounts are connected if their tweets are retweeted by the same user. Public tweets containing search queries related to data-driven journalism published from February 2017 to February 2018 were collected with Twitter real-time streaming application programming interface (API). A co-retweeted network with 1,148 accounts was derived from verified accounts’ retweeting posts. Results found that several communities emerged, and news organizations, nongovernmental and nonprofit professional organizations, and academic institutions were in the crucial positions of the network. The exponential random graph models (ERGMs) based on this network revealed the extent to which gender, geographical location, and institutional type of the users were associated with the tie-formation. This study documents the major actors who are discussing the subject of data-driven journalism and raises critical reflections toward the interdisciplinary collaboration in the production of public knowledge.

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InnoGPS for Data-Driven Exploration of Design Opportunities and Directions: The Case of Google Driverless Car Project

Journal of Mechanical Design ◽

10.1115/1.4037680 ◽

2017 ◽

Vol 139 (11) ◽

Cited By ~ 11

Author(s):

Jianxi Luo ◽

Bowen Yan ◽

Kristin Wood

Keyword(s):

Global Position System ◽

Physical Space ◽

User Preferences ◽

Data Driven ◽

Neighborhood Search ◽

Technology Firms ◽

Network Analytics ◽

Technology Competition ◽

Market Trends ◽

Shed Light

Engineers and technology firms must continually explore new design opportunities and directions to sustain or thrive in technology competition. However, the related decisions are normally based on personal gut feeling or experiences. Although the analysis of user preferences and market trends may shed light on some design opportunities from a demand perspective, design opportunities are always conditioned or enabled by the technological capabilities of designers. Herein, we present a data-driven methodology for designers to analyze and identify what technologies they can design for the next, based on the principle—what a designer can currently design condition or enable what it can design next. The methodology is centered on an empirically built network map of all known technologies, whose distances are quantified using more than 5 million patent records, and various network analytics to position a designer according to the technologies that they can design, navigate technologies in the neighborhood, and identify feasible paths to far fields for novel opportunities. Furthermore, we have integrated the technology space map, and various map-based functions for designer positioning, neighborhood search, path finding, and knowledge discovery and learning, into a data-driven visual analytic system named InnoGPS. InnoGPS is a global position system (GPS) for finding innovation positions and directions in the technology space, and conceived by analogy from the GPS that we use for positioning, neighborhood search, and direction finding in the physical space.

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The Cohesion-Based Communities of Symptoms of the Largest Component of the DSM-IV Network

Journal of Interconnection Networks ◽

10.1142/s0219265919400024 ◽

2019 ◽

Vol 19 (01) ◽

pp. 1940002

Author(s):

MOHAMMAD NAZMUL HAQUE ◽

PABLO MOSCATO

Keyword(s):

Mental Disorders ◽

Optimization Method ◽

Data Driven ◽

Dsm 5 ◽

Network Analytics ◽

Classification Of Diseases ◽

Data Driven Approach ◽

New Type ◽

Dsm Iv

Modern methods for network analytics provide an opportunity to revisit preconceived notions in the classification of diseases as “clusters of symptoms”. Curated collections which were subsequently modified, like the Diagnostic and Statistical Manuals of Mental Disorders “DSM-IV” and the most recent addition, DSM-5 allow us to introspect, using the solution provided by modern algorithms, if there exists a consensus between the clusters obtained via a data-driven approach, with the current classifications. In the case of mental disorders, the availability of a follow-up consensus collection (e.g. in this case the DSM-5), potentially allows investigating if the classification of disorders has moved closer (or away) to what a data-driven analytic approach would have unveiled by objectively inferring it from the data of DSM-IV. In this contribution, we present a new type of mathematical approach based on a global cohesion score which we introduce for the first time for the identification of communities of symptoms. Different from other approaches, this combinatorial optimization method is based on the identification of “triangles” in the network; these triads are the building block of feedback loops that can exist between groups of symptoms. We used a memetic algorithm to obtain a collection of highly connected-cohesive sets of symptoms and we compare the resulting community structure with the classification of disorders present in the DSM-IV.

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Data-driven network analytics and network optimisation in SDN-based programmable optical networks

2018 International Conference on Optical Network Design and Modeling (ONDM) ◽

10.23919/ondm.2018.8396137 ◽

2018 ◽

Cited By ~ 3

Author(s):

Shuangyi Yan ◽

Reza Nejabati ◽

Dimitra Simeonidou

Keyword(s):

Optical Networks ◽

Data Driven ◽

Network Analytics ◽

Network Optimisation

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The cohesion-based communities of symptoms of the largest component of the DSM-IV network

10.31234/osf.io/spd8k ◽

2018 ◽

Author(s):

Mohammad Nazmul Haque ◽

Pablo Moscato

Keyword(s):

Mental Disorders ◽

Optimization Method ◽

Data Driven ◽

Dsm 5 ◽

Network Analytics ◽

Classification Of Diseases ◽

Data Driven Approach ◽

New Type ◽

Dsm Iv

Modern methods for network analytics provide an opportunity to revisit preconceived notions in the classification of diseases as "clusters of symptoms''. Curated collections which were subsequently modified, like the Diagnostic and Statistical Manuals of Mental Disorders (DSM-IV and the most recent addition, DSM 5) allow us to introspect, using the solution provided by modern algorithms, if there exists a consensus between the clusters obtained via a data-driven approach, with the current classifications. In the case of mental disorders, the availability of a follow-up consensus collection (e.g. in this case the DSM 5), potentially allows to investigate if the classification of disorders has moved closer (or away) to what a data-driven analytic approach would have unveiled by objectively inferring it from the data of DSM-IV. In this contribution we present a new type of mathematical approach based on a global cohesion score which we introduce for the first time for the identification of communities of symptoms. Different from other approaches, this combinatorial optimization method is basedon the identification of "triangles'' in the network; these triads are the building block of feedback loops that can exist between groups of symptoms.We used a memetic algorithm to obtain a collection of highly connected-cohesive sets of symptoms and we compare the resulting community structure with the classification of disorders present in the DSM-IV.

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