Conference proceeding
Evaluation of a Reusable Technique for Refining Social Media Query Criteria for Crowd-Sourced Sentiment for Decision Making
2019 IEEE 20TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION FOR DATA SCIENCE (IRI 2019), pp 379-388
01 Jan 2019
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
There are three categories of users that consume social media data either for their personal use or for aggregation and presentation to others. These users rely on a preferential combination of Social Media Signals (SMS) that satisfies their information goals and aids in their decision-making. The research community is split on how to deal with some signals such as text originating from robotic voices; some suggest removing them while others are more interested in better identifying them. This paper statistically tests the SMS's in a dataset gathered during one of the political debates during the US Presidential Elections in 2016. It introduces a reusable technique aimed at contributing to the iterative and symbiotic user-system relationship, while improving the opportunity for arriving at empirically supported results for decision-making instances regardless of the consumer group.
Metrics
Details
- Title
- Evaluation of a Reusable Technique for Refining Social Media Query Criteria for Crowd-Sourced Sentiment for Decision Making
- Creators
- Kimberley Hemmings-Jarrett - Drexel UniversityJulian Jarrett - Drexel UniversityM. Brian Blake - Drexel UniversityIEEE Comp Soc
- Publication Details
- 2019 IEEE 20TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION FOR DATA SCIENCE (IRI 2019), pp 379-388
- Publisher
- IEEE
- Number of pages
- 10
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000635408000051
- Scopus ID
- 2-s2.0-85073208278
- Other Identifier
- 991019318926004721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications
- Computer Science, Software Engineering
- Engineering, Electrical & Electronic