Linking cyber and physical spaces through community detection and clustering in social media feeds

Croitoru, Arie; Wayant, Nicole M.; Crooks, Andrew; Radzikowski, Jacek; Stefanidis, Anthony

doi:10.1016/j.compenvurbsys.2014.11.002

Cited by 56 publications

(36 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They include: the development of new area/neighborhood profiles using social media data [88][89][90]; estimates of the mobile population at risk of crime [91]; the identification of "important" places in peoples' lives from mobile telephone data [92]; the detection and delineating of events [93,94]; analysis of regular mobility patterns [95,96]; classification of areas based on their Twitter temporal profile [97]; and a wealth of others. However, examples applied in the context of urban modeling, let alone ABM specifically, are much scarcer.…”

Section: Big Datamentioning

confidence: 99%

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Heppenstall

Malleson

Crooks

2016

Systems

View full text Add to dashboard Cite

Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach.

show abstract

Section: Big Datamentioning

confidence: 99%

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Heppenstall

Malleson

Crooks

2016

Systems

View full text Add to dashboard Cite

show abstract

“…In recent years, some attempts tried to show that community structures are one of the significant characteristics in the most complex networks such as social networks due to numerous trends of human being to forming groups or communities. Due to the significant applications of community detection, several community detection approaches have been presented in literature which can be classified into six categories: spectral and clustering methods [20], [21], [15], [22], hierarchical algorithms [23], modularity-based methods [24], [25], evolutionary modelbased methods [26], [27], local community detection methods, and feature-based assisted methods [11]. Along with that total sixteen articles (published in 2015 to 2017) presented in this survey are summarized in Table 1 that contains eight columns.…”

Section: Community Detection Over Snsmentioning

confidence: 99%

Review of Community Detection over Social Media:Graph Prospective

Jain¹,

Singh²

2019

ijacsa

View full text Add to dashboard Cite

Community over the social media is the group of globally distributed end users having similar attitude towards a particular topic or product. Community detection algorithm is used to identify the social atoms that are more densely interconnected relatively to the rest over the social media platform. Recently researchers focused on group-based algorithm and member-based algorithm for community detection over social media. This paper presents comprehensive overview of community detection technique based on recent research and subsequently explores graphical prospective of social media mining and social theory (Balance theory, status theory, correlation theory) over community detection. Along with that this paper presents a comparative analysis of three different state of art community detection algorithm available on I-Graph package on python i.e. walk trap, edge betweenness and fast greedy over six different social media data set. That yield intersecting facts about the capabilities and deficiency of community analysis methods.

show abstract

“…This has not only changed the traditional form of news reporting but also provided new opportunities for geographic science (Sui & Goodchild, 2011), given the rich geographic information attached to the social media data, often known as "geotags," in the form of longitude and latitude coordinates (Croitoru, Wayant, Crooks, Radzikowski, & Stefanidis, 2014;Lin & Cromley, 2015;Shelton, Poorthuis, Graham, & Zook, 2014). Scholars have applied social media data, Facebook, and microblogging, for example, into many fields of applied geographic studies, including population migration, urban space pattern, commuting behaviors, environmental event reactions, pandemics and disaster predictions, and crime occurrence (Cao et al, 2015;Char & Stow, 2015;Chunara, Andrews, & Brownstein, 2012;Croitoru et al, 2014;Gerber, 2014;Jang & Hart, 2015;Kounadi, Lampoltshammer, Groff, Sitko, & Leitner, 2015;Lampoltshammer, Kounadi, Sitko, & Hawelka, 2014;Lin & Cromley, 2015;Shelton et al, 2014;Patel & Jermacane, 2015;Widener & Li, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…In the recent past, geographers have shown growing interest in providing location-based services for urban residents (Croitoru et al, 2014). It is of great practical significance to screen and recommend the most popular restaurants to consumers, as dining is important to every urban dweller.…”

Section: Introductionmentioning

confidence: 99%