| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000433893 |
| 005 | | 20200226110433 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085778589 |
| 035 | |
▼a (MiAaPQ)AAI22619152 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 020 |
| 100 | 1 |
▼a Davis, Clayton A. |
| 245 | 10 |
▼a "Collect, Count, and Compare": Expanding Access and Scope of Social Media Analysis. |
| 260 | |
▼a [S.l.]:
▼b Indiana University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 116 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-03, Section: A. |
| 500 | |
▼a Advisor: Menczer, Filippo. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Indiana University, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a Today's online social networks (OSNs) with millions of users produce billions of data points daily, enabling observation and data collection at an unprecedented scale.Recent research has shown that social media can perform as `sensors' for collective activity at multiple scales. As a consequence, data extracted from social media platforms are increasingly used side-by-side with --- and sometimes even replacing --- traditional methods to investigate hard-pressing questions in the social, behavioral, and economic sciences.While undoubtedly useful, using online social media data in order to infer collective behavior is subject to some pitfalls, each of which raises a research question.First, raw data from social media platforms can be unwieldy for those that most want to make use of it: researchers, reporters, etc. How can we enable non-technical users to make use of OSN data? In order to bridge this gap, we created OSoMe, the Observatory on Social Media: an open infrastructure for sharing public data about information that is spread and collected through online social networks.The second of these pitfalls is that discourse on social media is vulnerable to manipulation. Can we detect manipulation in online social networks? Towards this end, we created Botometer, which allows users to quickly and easily analyze a Twitter account's publicly-available profile and activity history, returning a "bot score'" reflectingthe likelihood that the target account is automated, i.e., a bot.Finally we observe that online discussions of risky and/or stigmatizing behaviors can be unreliable. How else can we collect sensitive data while maintaining strict anonymity? In order to demonstrate such a data collection tool, we developed Kinsey Reporter: a global mobile survey platform to share, explore, and visualize anonymous data about sex. |
| 590 | |
▼a School code: 0093. |
| 650 | 4 |
▼a Information science. |
| 690 | |
▼a 0723 |
| 710 | 20 |
▼a Indiana University.
▼b Informatics. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-03A. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0093 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15493599
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |