Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Predicting Friendship Levels in Online Social Networks
Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
2010 (engelsk)Independent thesis Advanced level (degree of Master (Two Years))Oppgave
Abstract [en]

Abstract Context: Online social networks such as Facebook, Twitter, and MySpace have become the preferred interaction, entertainment and socializing facility on the Internet. However, these social network services also bring privacy issues in more limelight than ever. Several privacy leakage problems are highlighted in the literature with a variety of suggested countermeasures. Most of these measures further add complexity and management overhead for the user. One ignored aspect with the architecture of online social networks is that they do not offer any mechanism to calculate the strength of relationship between individuals. This information is quite useful to identify possible privacy threats. Objectives: In this study, we identify users’ privacy concerns and their satisfaction regarding privacy control measures provided by online social networks. Furthermore, this study explores data mining techniques to predict the levels/intensity of friendship in online social networks. This study also proposes a technique to utilize predicted friendship levels for privacy preservation in a semi-automatic privacy framework. Methods: An online survey is conducted to analyze Facebook users’ concerns as well as their interaction behavior with their good friends. On the basis of survey results, an experiment is performed to justify practical demonstration of data mining phases. Results: We found that users are concerned to save their private data. As a precautionary measure, they restrain to show their private information on Facebook due to privacy leakage fears. Additionally, individuals also perform some actions which they also feel as privacy vulnerability. This study further identifies that the importance of interaction type varies while communication. This research also discovered, “mutual friends” and “profile visits”, the two non-interaction based estimation metrics. Finally, this study also found an excellent performance of J48 and Naïve Bayes algorithms to classify friendship levels. Conclusions: The users are not satisfied with the privacy measures provided by the online social networks. We establish that the online social networks should offer a privacy mechanism which does not require a lot of privacy control effort from the users. This study also concludes that factors such as current status, interaction type need to be considered with the interaction count method in order to improve its performance. Furthermore, data mining classification algorithms are tailor-made for the prediction of friendship levels.

sted, utgiver, år, opplag, sider
2010. , s. 65
Emneord [en]
Online Social Network, Friendship Levels, Privacy Concerns, Data Mining
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-3351Lokal ID: oai:bth.se:arkivex17CC0BC3AF6D0A7CC125778900012E5DOAI: oai:DiVA.org:bth-3351DiVA, id: diva2:830656
Uppsök
Technology
Veileder
Tilgjengelig fra: 2015-04-22 Laget: 2010-08-24 Sist oppdatert: 2018-01-11bibliografisk kontrollert

Open Access i DiVA

fulltekst(1626 kB)627 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 1626 kBChecksum SHA-512
92a48753e4fe783a76ad802b014b96ed711b24fc3ac49a802850e7450850c40d9119fd8c1df3d3dfa8de6231c17ddd00fb469c7add61c22d2c140067714bc185
Type fulltextMimetype application/pdf

Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 627 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 819 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf