2015
DOI: 10.21236/ad1007379
|View full text |Cite
|
Sign up to set email alerts
|

Computing Distrust in Social Media

Abstract: A myriad of social media services are emerging in recent years that allow people to communicate and express themselves conveniently and easily. The pervasive use of social media generates massive data at an unprecedented rate. It becomes increasingly difficult for online users to find relevant information or, in other words, exacerbates the information overload problem. Meanwhile, users in social media can be both passive content consumers and active content producers, causing the quality of user-generated con… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
6
0

Year Published

2015
2015
2022
2022

Publication Types

Select...
2
1
1

Relationship

1
3

Authors

Journals

citations
Cited by 4 publications
(6 citation statements)
references
References 91 publications
0
6
0
Order By: Relevance
“…Without distrust, trust computing might be biased [127]. For example, for the adjacency matrix in Figure 1.3, a zero may denote there is no opinion from a user to another user, or there is a distrust relation between them although the former is a more common assumption.…”
Section: Incorporating Distrustmentioning
confidence: 99%
See 2 more Smart Citations
“…Without distrust, trust computing might be biased [127]. For example, for the adjacency matrix in Figure 1.3, a zero may denote there is no opinion from a user to another user, or there is a distrust relation between them although the former is a more common assumption.…”
Section: Incorporating Distrustmentioning
confidence: 99%
“…Untrust (or no trust) and distrust are not the same [127]. e former means that there is no evaluation from one user to another; the latter distrust shows that there is a distrust evaluation from one user to another.…”
Section: Trust Untrust and Distrustmentioning
confidence: 99%
See 1 more Smart Citation
“…In this paper, as a second contribution, we propose to compare the use of the local reputation vs the simple reliability when using the algorithm U2G for automatically forming groups in virtual communities. Our experiments, performed on the real data extracted from virtual communities EPINIONS and CIAO [8], in which users provide reviews concerning commercial products falling in different categories, clearly show that the use of local reputation outperforms the results obtained using the reliability.…”
Section: Using Local Reputation Rather Than Reliabilitymentioning
confidence: 86%
“…The used datasets, extracted from the social networks CIAO and EPINIONS, have been described in [41] and can be downloaded at [8]. CIAO (resp.…”
Section: Methodsmentioning
confidence: 99%