dc.contributor.author |
Mittal, Manasi |
|
dc.contributor.author |
Asghar, Muhammad Rizwan |
|
dc.contributor.author |
Tripathi, Arvind |
|
dc.contributor.editor |
Wang, GJ |
|
dc.contributor.editor |
Ko, R |
|
dc.contributor.editor |
Bhuiyan, MZA |
|
dc.contributor.editor |
Pan, Y |
|
dc.coverage.spatial |
Guangzhou, PEOPLES R CHINA |
|
dc.date.accessioned |
2021-12-06T02:25:10Z |
|
dc.date.available |
2021-12-06T02:25:10Z |
|
dc.date.issued |
2021-1-1 |
|
dc.identifier.citation |
2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2020, pp. 1228-1235 |
|
dc.identifier.isbn |
9781665403924 |
|
dc.identifier.issn |
2324-898X |
|
dc.identifier.uri |
https://hdl.handle.net/2292/57643 |
|
dc.description.abstract |
Social media has become an integral part of modernday society. With increasingly digital societies, individuals have become more familiar and comfortable in using Online Social Networks (OSNs) for just about every aspect of their lives. This higher level of comfort leads to users spilling their emotions on OSNs and eventually their private information. In this work, we aim to investigate the relationship between users' emotions and private information in their tweets. Our research question is whether users' emotions, expressed in their tweets, affect their likelihood to reveal their own private information (privacy leakage) in subsequent tweets. In contrast to existing survey-based approaches, we use an inductive, data-driven approach to answer our research question. We use state-of-the-art techniques to classify users' emotions, and privacy scoring and employ a new technique involving BERT for binary detection of sensitive data. We use two parallel classification frameworks: one that takes the user's emotional state into account and the other for the detection of sensitive data in tweets. Consecutively, we identify individual cases of correlation between the two. We bring the two classifiers together to interpret the changes in both factors over time during a conversation between individuals. Variations were found with respect to the kinds of private information revealed in different states. Our results show that being in negative emotional states, such as sadness, anger or fear, leads to higher privacy leakage than otherwise. |
|
dc.publisher |
IEEE |
|
dc.relation.ispartof |
2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) |
|
dc.relation.ispartofseries |
2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights |
2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.rights.uri |
https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/#accepted |
|
dc.subject |
Science & Technology |
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dc.subject |
Technology |
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dc.subject |
Computer Science, Hardware & Architecture |
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dc.subject |
Computer Science, Information Systems |
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dc.subject |
Computer Science, Theory & Methods |
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dc.subject |
Telecommunications |
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dc.subject |
Computer Science |
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dc.subject |
Privacy |
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dc.subject |
Emotions |
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dc.subject |
Twitter |
|
dc.title |
Do My Emotions Influence What I Share? Analysing the Effects of Emotions on Privacy Leakage in Twitter |
|
dc.type |
Conference Item |
|
dc.identifier.doi |
10.1109/trustcom50675.2020.00165 |
|
pubs.begin-page |
1228 |
|
pubs.volume |
00 |
|
dc.date.updated |
2021-11-13T21:17:11Z |
|
dc.rights.holder |
Copyright: IEEE |
en |
pubs.author-url |
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000671077600151&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e41486220adb198d0efde5a3b153e7d |
|
pubs.end-page |
1235 |
|
pubs.finish-date |
2021-1-1 |
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pubs.publication-status |
Published |
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pubs.start-date |
2020-12-29 |
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dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.elements-id |
842636 |
|