Event Trend Detector

Ward, Ryan; Lee, Jun; Beard, Stuart; Edwards, Skylar; Su, Spencer

Event Trend Detector

dc.contributor.author	Ward, Ryan	en
dc.contributor.author	Lee, Jun	en
dc.contributor.author	Beard, Stuart	en
dc.contributor.author	Edwards, Skylar	en
dc.contributor.author	Su, Spencer	en
dc.date.accessioned	2018-05-10T06:54:25Z	en
dc.date.available	2018-05-10T06:54:25Z	en
dc.date.issued	2018-05-07	en
dc.description.abstract	The Global Event and Trend Archive Research (GETAR) project is supported by NSF (IIS-1619028 and 1619371) through 2019. It will devise interactive, integrated, digital library/archive systems coupled with linked and expert-curated webpage/tweet collections. In support of GETAR, the 2017 project built a tool to scrape the news to identify important global events. It generates seeds (URLs of relevant webpages, as well as Twitter-related hashtags and keywords and mentions). A display of the results can be seen from the hall outside 2030 Torgersen Hall. This project extends that work in multiple ways. The quality of the work done has been improved. This is evident in changes done to the clustering algorithm and the user interface changes to the clustering display of global events. Second, in addition to events reported in the news, trends have been identified, and a database of trends and related events were built with a corresponding user interface according to the client’s preferences. Third, the results of the detection are connected to software for collecting tweets and crawling webpages, so automated daily runs find and archive webpages related to each trend and event. The final deliverables include development of a trend detection feature with Reddit news, integration of Google Trends into trend detection, an improved clustering algorithm to have more accurate clusters according to k-means, an improved UI for important global events according to what the client wanted, and an aesthetically pleasing UI to display the trend information. Work accomplished included setting up a table of tagged entities for trend detection and configuring the database for clustering and trends to work with our personal machines, and completing the deliverables. Many lessons were learned regarding the importance of using existing tools, starting early, doing research, having regular meetings, and having good documentation.	en
dc.description.notes	EventTrendDetectorReport: This file goes over the project in its entirety from start to finish. It includes a developer's manual, user manual and key design aspects. Reference this file for any questions about the trend detection project. See Word and PDF versions. EventTrendDetectorPresentation: This file represents the final presentation given on the trend detector. Key design aspects and technologies used are referenced in this presentation. See PowerPoint and PDF versions. CS4624-EventTrendDetector: This zip file contains the necessary python scripts and php files to run this code on a local machine. Additionally there are 20 svg files containing graph data from Reddit and Google.	en
dc.description.sponsorship	NSF (IIS-1619028 and 1619371)	en
dc.identifier.uri	http://hdl.handle.net/10919/83205	en
dc.language.iso	en_US	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Trend Detection	en
dc.subject	Trends	en
dc.subject	Python	en
dc.subject	GETAR	en
dc.subject	Reddit	en
dc.subject	Google	en
dc.subject	News trends	en
dc.title	Event Trend Detector	en
dc.type	Presentation	en
dc.type	Report	en
dc.type	Software	en