School of Electronics, Electrical Engineering and Computer Science

A System for Inferring Tourism Statistics based on Twitter Data

Internships Summer 2017/18

Proposed Project Title:

  • A System for Inferring Tourism Statistics based on Twitter Data

Principal Supervisor(s):
  • Cheng Long


Project Description:

A conventional approach for generating tourism statistics (e.g., how many tourists visit and stay at a destination) is to collect data through surveys and interviews at the airports/borders which is costly since manual efforts are involved. Essentially, tourism statistics are based on the data about tourists' physical movements (e.g., visits to a destination from outside). This data can hardly be acquired by other ways other than surveys and interviews in the past, but this is no longer case nowadays. In fact, the data is covered by the Twitter data partially. For example, a tourist from US might post a tweet with a geo-tag of "Belfast" when he/she visits Belfast, and this reveals his/her presence at Belfast. Thus, it is interesting to explore the feasibility to use Twitter data to inferring the tourism statistics. The basic idea is to collect the Twitter data that reveals the physical presences of users at a destination (e.g., Belfast) and then to apply some machine learning methods such as regression to infer the tourism statistics at the destination. A case study could be done to infer the tourism statistics for Belfast and NI.

Tasks that are involved in this project include:

  1. downloading/collecting/crawling some Twitter data;
  2. preprocess the data (e.g., filtering those data that are not related to a specific location such Belfast);
  3. applying some machine learning methods (e.g., regression methods) to infer the tourism statistics.

Objectives:

To develop a system/prototype that could be used to infer the tourism statistics at Belfast/NI/UK.


Academic Requirements:

The scheme is open to all EEECS Undergraduates (apart from students on the BIT degree pathway and students who are due to graduate this summer)


General Information:

Each internship will last between 6-8 weeks and will pay a weekly stipend of £200.

Accommodation and travel costs are not provided under this scheme.

Start date: 15 June, 2017

Duration: 8 (Weeks)

Location: 18 Malone Road, Belfast

Further information available at: http://www.qub.ac.uk/schools/eeecs/Research/


Contact details:

Supervisor Name: Cheng Long
Address:

Queens University of Belfast
School of EEECS,
Computer Science Building,
18 Malone Road,
Belfast
BT9 5BN

Email: cheng.long@qub.ac.uk
Tel: +44 (0)28 9097 4783