Editing Social network analysis (section)

==Practical applications==
{{see also|Social network analysis in criminology}}
Social network analysis is used extensively in a wide range of applications and disciplines.  Some common network analysis applications include data aggregation and [[data mining|mining]], network propagation modeling, network modeling and sampling, user attribute and behavior analysis, community-maintained resource support, location-based interaction analysis, [[social sharing]] and filtering, [[recommender system]]s development, and [[link prediction]] and entity resolution.<ref name="Golbeck" /> In the private sector, businesses use social network analysis to support activities such as customer interaction and analysis, [[information system]] development analysis,<ref>{{cite journal |last1=Aram |first1=Michael |last2=Neumann |first2=Gustaf |title=Multilayered analysis of co-development of business information systems |journal=Journal of Internet Services and Applications |date=1 July 2015 |volume=6 |issue=1 |pages=13 |doi=10.1186/s13174-015-0030-8 |s2cid=16502371 |doi-access=free }}</ref> marketing, and [[business intelligence]] needs (see [[social media analytics]]).  Some public sector uses include development of leader engagement strategies, analysis of individual and group engagement and [[media use]], and [[Collaborative problem-solving group|community-based problem solving]].

=== Longitudinal SNA in schools ===
Large numbers of researchers worldwide examine the social networks of children and adolescents. In questionnaires, they list all classmates, students in the same grade, or schoolmates, asking: "Who are your best friends?". Students may sometimes nominate as many peers as they wish; other times, the number of nominations is limited. Social network researchers have investigated similarities in friendship networks. The similarity between friends was established as far back as classical antiquity.<ref>{{Cite journal |last1=McPherson |first1=Miller |last2=Smith-Lovin |first2=Lynn |last3=Cook |first3=James M |date=2001 |title=Birds of a Feather: Homophily in Social Networks |url=https://www.annualreviews.org/doi/10.1146/annurev.soc.27.1.415 |journal=Annual Review of Sociology |language=en |volume=27 |issue=1 |pages=415–444 |doi=10.1146/annurev.soc.27.1.415 |s2cid=2341021 |issn=0360-0572}}</ref> Resemblance is an important basis for the survival of friendships. Similarity in characteristics, attitudes, or behaviors means that friends understand each other more quickly, have common interests to talk about, know better where they stand with each other, and have more trust in each other.<ref>{{Cite journal |last1=Laursen |first1=Brett |last2=Veenstra |first2=René |date=2021 |title=Toward understanding the functions of peer influence: A summary and synthesis of recent empirical research |journal=Journal of Research on Adolescence |language=en |volume=31 |issue=4 |pages=889–907 |doi=10.1111/jora.12606 |issn=1050-8392 |pmc=8630732 |pmid=34820944}}</ref> As a result, such relationships are more stable and valuable. Moreover, looking more alike makes young people more confident and strengthens them in developing their identity.<ref>{{Cite journal |title=Hallinan, M. T. (1980). Patterns of cliquing among youth. In H. C. Foot, A. J. Chapman, & J. R. Smith (Eds.), Friendship and social relations in children (pp. 321–342). New York, NY: Wiley. |url=https://psycnet.apa.org/record/1995-97220-012 |access-date=2023-03-10 |journal=Apa Psycnet |date=1980 |language=en |last1=T. |first1=Hallinan }}</ref> Similarity in behavior can result from two processes: selection and influence. These two processes can be distinguished using longitudinal social network analysis in the R package SIENA (Simulation Investigation for Empirical Network Analyses), developed by [[Tom Snijders]] and colleagues.<ref>{{Cite journal |last1=Snijders |first1=Tom A. B. |last2=van de Bunt |first2=Gerhard G. |last3=Steglich |first3=Christian E. G. |date=2010 |title=Introduction to stochastic actor-based models for network dynamics |url=https://www.sciencedirect.com/science/article/pii/S0378873309000069 |journal=Social Networks |series=Dynamics of Social Networks |language=en |volume=32 |issue=1 |pages=44–60 |doi=10.1016/j.socnet.2009.02.004 |issn=0378-8733}}</ref> Longitudinal social network analysis became mainstream after the publication of a special issue of the ''[[Journal of Research on Adolescence]]'' in 2013, edited by [[René Veenstra]] and containing 15 empirical papers.<ref>{{Cite journal |last1=Veenstra |first1=René |last2=Laninga-Wijnen |first2=Lydia |year=2023 |title=The Prominence of Peer Interactions, Relationships, and Networks in Adolescence and Early Adulthood |url=https://osf.io/preprints/socarxiv/s57zm/ |access-date=2023-03-10 |website=osf.io |publisher=American Psychological Association}}</ref>

=== Security applications ===
Social network analysis is also used in intelligence, [[counter-intelligence]] and [[law enforcement]] activities. This technique allows the analysts to map covert organizations such as an [[espionage]] ring, an organized crime family or a street gang. The [[National Security Agency]] (NSA) uses its [[computer surveillance|electronic surveillance]] programs to generate the data needed to perform this type of analysis on terrorist cells and other networks deemed relevant to national security. The NSA looks up to three nodes deep during this network analysis.<ref name="nsa_degrees">{{cite news|url=https://www.theguardian.com/world/2013/jul/17/nsa-surveillance-house-hearing|date=17 July 2013|access-date=19 July 2013|title=NSA warned to rein in surveillance as agency reveals even greater scope|newspaper=The Guardian|last1=Ackerman|first1=Spencer}}</ref> After the initial mapping of the social network is complete, analysis is performed to determine the structure of the network and determine, for example, the leaders within the network.<ref name="nsa_how">{{cite web|url=http://www.digitaltonto.com/2013/how-the-nsa-uses-social-network-analysis-to-map-terrorist-networks/|date=12 June 2013|access-date=19 Jul 2013|title=How The NSA Uses Social Network Analysis To Map Terrorist Networks}}</ref> This allows military or law enforcement assets to launch capture-or-kill [[decapitation attack]]s on the [[high-value targets]] in leadership positions to disrupt the functioning of the network.
The NSA has been performing social network analysis on [[call detail record]]s (CDRs), also known as [[metadata]], since shortly after the [[September 11 attacks]].<ref name="NSA_SNA">{{cite magazine|url=https://www.wired.com/science/discoveries/news/2006/05/70888|title=NSA Using Social Network Analysis|magazine=Wired|date=12 May 2006|access-date=19 July 2013}}</ref><ref name="nsa_usa">{{cite journal|url=http://www.slate.com/articles/news_and_politics/explainer/2006/05/how_the_nsa_does_social_network_analysis.html|date=11 May 2006|access-date=19 July 2013|title=NSA has massive database of Americans' phone calls |journal=Slate |last1=Dryer |first1=Alexander }}</ref>

=== Textual analysis applications ===
Large textual corpora can be turned into networks and then analyzed using social network analysis. In these networks, the nodes are Social Actors, and the links are Actions. The extraction of these networks can be automated by using parsers. The resulting networks, which can contain thousands of nodes, are then analyzed using tools from network theory to identify the key actors, the key communities or parties, and general properties such as the robustness or structural stability of the overall network or the centrality of certain nodes.<ref>{{cite journal |last1=Sudhahar |first1=Saatviga |last2=De Fazio |first2=Gianluca |last3=Franzosi |first3=Roberto |last4=Cristianini |first4=Nello |title=Network analysis of narrative content in large corpora |journal=Natural Language Engineering |date=January 2015 |volume=21 |issue=1 |pages=81–112 |doi=10.1017/S1351324913000247 |hdl=1983/dfb87140-42e2-486a-91d5-55f9007042df |s2cid=3385681 |url=https://research-information.bris.ac.uk/en/publications/dfb87140-42e2-486a-91d5-55f9007042df |hdl-access=free }}</ref> This automates the approach introduced by Quantitative Narrative Analysis,<ref>Quantitative Narrative Analysis; Roberto Franzosi; Emory University © 2010</ref> whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.<ref name="ReferenceA" />
[[File:Tripletsnew2012.png|thumb|right|Narrative network of US Elections 2012<ref name="ReferenceA">{{cite journal |last1=Sudhahar |first1=Saatviga |last2=Veltri |first2=Giuseppe A |last3=Cristianini |first3=Nello |title=Automated analysis of the US presidential elections using Big Data and network analysis |journal=Big Data & Society |date=May 2015 |volume=2 |issue=1 |doi=10.1177/2053951715572916 |doi-access=free |hdl=2381/31767 |hdl-access=free }}</ref>]]
In other approaches, textual analysis is carried out considering the network of words co-occurring in a text. In these networks, nodes are words and links among them are weighted based on their frequency of co-occurrence (within a specific maximum range).

=== Internet applications ===
Social network analysis has also been applied to understanding online behavior by individuals, organizations, and between websites.<ref name=Ghanbarnejad/> [[Hyperlink]] analysis can be used to analyze the connections between [[website]]s or [[Web page|webpages]] to examine how information flows as individuals navigate the web.<ref>{{cite journal |last1=Osterbur |first1=Megan |last2=Kiel |first2=Christina |title=A hegemon fighting for equal rights: the dominant role of COC Nederland in the LGBT transnational advocacy network |journal=Global Networks |date=April 2017 |volume=17 |issue=2 |pages=234–254 |doi=10.1111/glob.12126 }}</ref> The connections between organizations has been analyzed via hyperlink analysis to examine which organizations within an issue community.<ref>{{cite book |chapter-url=https://www.degruyter.com/document/doi/10.18574/nyu/9781479849468.003.0034/html |chapter-url-access=subscription |doi=10.18574/nyu/9781479849468.003.0034 |isbn=978-1-4798-4946-8 |chapter=Pink Links: Visualizing the Global LGBTQ Network |date=19 September 2017 |publisher=New York University Press |editor1-first=Marla |editor1-last=Brettschneider |editor2-first=Susan |editor2-last=Burgess |editor3-first=Christine |editor3-last=Keating |title=LGBTQ Politics |pages=493–522 }}</ref>

==== Netocracy ====
Another concept that has emerged from this connection between social network theory and the Internet is the concept of [[netocracy]], where several authors have emerged studying the correlation between the extended use of online social networks, and changes in social power dynamics.<ref>{{cite book|last1=Bard|first1=Alexander|last2=Sšderqvist|first2=Jan|title=The Netocracts: Futurica Trilogy 1|date=24 February 2012 |publisher=Stockholm Text|isbn=9789187173004|url=https://books.google.com/books?id=TeWCBwAAQBAJ&pg=PT131|access-date=3 March 2017|language=en}}</ref>

==== Social media internet applications ====
Social network analysis has been applied to social media as a tool to understand behavior between individuals or organizations through their linkages on social media websites such as [[Twitter]] and [[Facebook]].<ref>{{Cite book|last1=Kwak|first1=Haewoon|last2=Lee|first2=Changhyun|last3=Park|first3=Hosung|last4=Moon|first4=Sue|title=Proceedings of the 19th international conference on World wide web |chapter=What is Twitter, a social network or a news media? |date=2010-04-26|publisher=ACM|pages=591–600|doi=10.1145/1772690.1772751|isbn=9781605587998|citeseerx=10.1.1.212.1490|s2cid=207178765}}</ref>

===In computer-supported collaborative learning===
One of the most current methods of the application of SNA is to the study of [[computer-supported collaborative learning]] (CSCL). When applied to CSCL, SNA is used to help understand how learners collaborate in terms of amount, frequency, and length, as well as the quality, topic, and strategies of communication.<ref name=":0">{{Cite journal|last1=Laat|first1=Maarten de|last2=Lally|first2=Vic|last3=Lipponen|first3=Lasse|last4=Simons|first4=Robert-Jan|date=2007-03-08|title=Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis|journal=International Journal of Computer-Supported Collaborative Learning|language=en|volume=2|issue=1|pages=87–103|doi=10.1007/s11412-007-9006-4|s2cid=3238474}}</ref> Additionally, SNA can focus on specific aspects of the network connection, or the entire network as a whole. It uses graphical representations, written representations, and data representations to help examine the connections within a CSCL network.<ref name=":0" /> When applying SNA to a CSCL environment the interactions of the participants are treated as a social network. The focus of the analysis is on the "connections" made among the participants – how they interact and communicate – as opposed to how each participant behaved on his or her own.

====Key terms====
There are several key terms associated with social network analysis research in computer-supported collaborative learning such as: '''density''', '''centrality''', '''indegree''', '''outdegree''', and '''sociogram'''.
* '''Density''' refers to the "connections" between participants.  Density is defined as the number of connections a participant has, divided by the total possible connections a participant could have.  For example, if there are 20 people participating, each person could potentially connect to 19 other people. A density of 100% (19/19) is the greatest density in the system.  A density of 5% indicates there is only 1 of 19 possible connections.<ref name=":0" />
* '''Centrality''' focuses on the behavior of individual participants within a network.  It measures the extent to which an individual interacts with other individuals in the network.  The more an individual connects to others in a network, the greater their centrality in the network.<ref name=":0" /><ref name=":1" />

In-degree and out-degree variables are related to centrality.
* '''In-degree''' centrality concentrates on a specific individual as the point of focus; centrality of all other individuals is based on their relation to the focal point of the "in-degree" individual.<ref name=":0" />
* '''Out-degree''' is a measure of centrality that still focuses on a single individual, but the analytic is concerned with the out-going interactions of the individual; the measure of out-degree centrality is how many times the focus point individual interacts with others.<ref name=":0" /><ref name=":1" />
* A '''sociogram''' is a visualization with defined boundaries of connections in the network. For example, a sociogram which shows out-degree centrality points for Participant A would illustrate all outgoing connections Participant A made in the studied network.<ref name=":0" />

====Unique capabilities====
Researchers employ social network analysis in the study of computer-supported collaborative learning in part due to the unique capabilities it offers. This particular method allows the study of interaction patterns within a [[Networked learning|networked learning community]] and can help illustrate the extent of the participants' interactions with the other members of the group.<ref name=":0" /> The graphics created using SNA tools provide visualizations of the connections among participants and the strategies used to communicate within the group. Some authors also suggest that SNA provides a method of easily analyzing changes in participatory patterns of members over time.<ref>{{cite book |doi=10.4324/9780203763865-71 |chapter=Patterns of Interaction in Computer-supported Learning: A Social Network Analysis |title=International Conference of the Learning Sciences |year=2013 |pages=346–351 |isbn=9780203763865 }}</ref>

A number of research studies have applied SNA to CSCL across a variety of contexts. The findings include the correlation between a network's density and the teacher's presence,<ref name=":0" /> a greater regard for the recommendations of "central" participants,<ref>{{cite journal |last1=Martı́nez |first1=A. |last2=Dimitriadis |first2=Y. |last3=Rubia |first3=B. |last4=Gómez |first4=E. |last5=de la Fuente |first5=P. |title=Combining qualitative evaluation and social network analysis for the study of classroom social interactions |journal=Computers & Education |date=December 2003 |volume=41 |issue=4 |pages=353–368 |doi=10.1016/j.compedu.2003.06.001 |citeseerx=10.1.1.114.7474 |s2cid=10636524 }}</ref> infrequency of cross-gender interaction in a network,<ref>{{cite conference|author1=Cho, H.|author2=Stefanone, M.|author3=Gay, G|name-list-style=amp|year=2002|title=Social information sharing in a CSCL community|conference=Computer support for collaborative learning: Foundations for a CSCL community|location=Hillsdale, NJ|publisher=Lawrence Erlbaum|pages=43–50|citeseerx=10.1.1.225.5273}}</ref> and the relatively small role played by an instructor in an [[asynchronous learning]] network.<ref>{{cite journal|author1=Aviv, R.|author2=Erlich, Z.|author3=Ravid, G.|author4=Geva, A.|name-list-style=amp|year=2003|title=Network analysis of knowledge construction in asynchronous learning networks|journal=Journal of Asynchronous Learning Networks|volume=7|issue=3|pages=1–23|citeseerx=10.1.1.2.9044}}</ref>

====Other methods used alongside SNA====
Although many studies have demonstrated the value of social network analysis within the computer-supported collaborative learning field,<ref name=":0" /> researchers have suggested that SNA by itself is not enough for achieving a full understanding of CSCL. The complexity of the interaction processes and the myriad sources of data make it difficult for SNA to provide an in-depth analysis of CSCL.<ref>{{Cite book|title=Groupware: Design, Implementation, and Use|last1=Daradoumis|first1=Thanasis|last2=Martínez-Monés|first2=Alejandra|last3=Xhafa|first3=Fatos|chapter=An Integrated Approach for Analysing and Assessing the Performance of Virtual Learning Groups |date=2004-09-05|publisher=Springer Berlin Heidelberg|isbn=9783540230168|editor-last=Vreede|editor-first=Gert-Jan de|series=Lecture Notes in Computer Science|volume=3198 |pages=[https://archive.org/details/unset0000unse_i0a6/page/289 289–304]|language=en|doi=10.1007/978-3-540-30112-7_25|editor-last2=Guerrero|editor-first2=Luis A.|editor-last3=Raventós|editor-first3=Gabriela Marín|hdl=2117/116654|s2cid=6605 |chapter-url-access=registration|chapter-url=https://archive.org/details/unset0000unse_i0a6/page/289}}</ref> Researchers indicate that SNA needs to be complemented with other methods of analysis to form a more accurate picture of collaborative learning experiences.<ref name=autogenerated1/>

A number of research studies have combined other types of analysis with SNA in the study of CSCL. This can be referred to as a multi-method approach or data [[Triangulation (social science)|triangulation]], which will lead to an increase of evaluation [[Reliability (statistics)|reliability]] in CSCL studies.
* Qualitative method – The principles of qualitative case study research constitute a solid framework for the integration of SNA methods in the study of CSCL experiences.<ref>{{Cite journal|last=Johnson|first=Karen E.|date=1996-01-01|title=Review of The Art of Case Study Research|jstor=329758|journal=The Modern Language Journal|volume=80|issue=4|pages=556–557|doi=10.2307/329758}}</ref>
** ''[[Ethnography|Ethnographic]] data'' such as student questionnaires and interviews and classroom non-participant observations<ref name=autogenerated1>{{cite journal|author1=Martínez, A.|author2=Dimitriadis, Y.|author3=Rubia, B.|author4=Gómez, E.|author5=de la Fuente, P.|date=2003-12-01|title=Combining qualitative evaluation and social network analysis for the study of classroom social interactions|journal=Computers & Education. Documenting Collaborative Interactions: Issues and Approaches|volume=41|issue=4|pages=353–368|doi=10.1016/j.compedu.2003.06.001|citeseerx=10.1.1.114.7474|s2cid=10636524 }}</ref>
** ''[[Case study|Case studies]]'': comprehensively study particular CSCL situations and relate findings to general schemes<ref name=autogenerated1 />
** ''[[Content analysis]]:'' offers information about the content of the communication among members<ref name=autogenerated1 />
* Quantitative method – This includes simple descriptive statistical analyses on occurrences to identify particular attitudes of group members who have not been able to be tracked via SNA in order to detect general tendencies.
** ''Computer [[Logfile|log files]]:'' provide automatic data on how collaborative tools are used by learners<ref name=autogenerated1 />
** ''[[Multidimensional scaling|Multidimensional scaling (MDS)]]'': charts similarities among actors, so that more similar input data is closer together<ref name=autogenerated1 />
** ''[[Software]] tools:'' QUEST, SAMSA (System for Adjacency Matrix and Sociogram-based Analysis), and Nud*IST<ref name=autogenerated1 />