Social media analytics and research testbed (SMART)
Exploring spatiotemporal patterns of human dynamics with geo-targeted social media messages
Bibliographic Data
| ID | 5260867 |
|---|---|
| Authors | Jiue-An Yang, Jiue‐an Yang (0000-0003-4246-0470, San Diego State University), Ming-Hsiang Tsou (0000-0003-3421-486X, San Diego State University, corresponding author), Chin-Te Jung, Chin‐Te Jung (San Diego State University), Christopher Allen (0000-0002-5429-9437, San Diego State University), Brian H Spitzberg (0000-0003-3838-6052, San Diego State University), Jean Mark Gawron (0000-0001-6288-4014, San Diego State University), Su-Yeon Han (San Diego State University) |
| Year | 2016 |
| Volume | 3 |
| Issue | 1 |
| Publication date | 2016-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Big Data & Society (JOURNAL) |
| Journal identifiers | ISSN: 2053-9517 • E-ISSN: 2053-9517 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/2053951716652914 |
| OpenAlex | W2467333773 |
| Language | EN |
| Citations received | 4 |
| References cited | 54 |
The multilevel model of meme diffusion conceptualizes how mediated messages diffuse over time and space. As a pilot application of implementing the meme diffusion, we developed the social media analytics and research testbed to monitor Twitter messages and track the diffusion of information in and across different cities and geographic regions. Social media analytics and research testbed is an online geo-targeted search and analytics tool, including an automatic data processing procedure at the backend and an interactive frontend user interface. Social media analytics and research testbed is initially designed to facilitate (1) searching and geo-locating tweet topics and terms in different cities and geographic regions; (2) filtering noise from raw data (such as removing redundant retweets and using machine learning methods to improve precision); (3) analyzing social media data from a spatiotemporal perspective; and (4) visualizing social media data in diagnostic ways (such as weekly and monthly trends, trend maps, top media, top retweets, top mentions, or top hashtags). Social media analytics and research testbed provides researchers and domain experts with a tool that can efficiently facilitate the refinement, formalization, and testing of research hypotheses or questions. Three case studies (flu outbreaks, Ebola epidemic, and marijuana legalization) are introduced to illustrate how the predictions of meme diffusion can be examined and to demonstrate the potentials and key functions of social media analytics and research testbed
Analytics · Big data · Data Analysis · Data mining · Data science · Social media · Social media analytics · Testbed · World Wide Web · Complex Network Analysis Techniques · Computer Science · Data-Driven Disease Surveillance · Human Mobility and Location-Based Analysis
Detecting influenza epidemics using search engine query data
Earthquake shakes Twitter users
Where in the World Are You? Geolocation and Language Identification in Twitter
Term-weighting approaches in automatic text retrieval
Warning tweets
The impact of electronic word-of-mouth communication
Geography of Twitter networks
POI Pulse
Visualization of social media
Research challenges and opportunities in mapping social media and Big Data
Geo-located Twitter as proxy for global mobility patterns
Mapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing)
Families and Networks of Internet Memes
Universitycrisis
Searching the Web for Influenza Vaccines
Toward A Model of Meme Diffusion (M 3 D)
| Citations per year | 0.5 |
|---|---|
| Citation span | 2018 - 2023 (6) |
| Highly cited | No |