Text mining in social media for

participatory sensing data

A dissertation by

Georgios Keikoglou

SID: 3301100005

Participatory sensing

Information Analysis

Data Mining Text Mining

Web Mining

Main idea:

Mining citizen’s observations

Social Media Online Social Communities Blogs Podcasts Forums Wikis Content communities

Twitter Micro-blogging service Short messages (140 characters), known as tweets Information network and a news source

Text mining methodology Available text mining tools

Social Mention Trending

Specific keywords with geographical assessment “Ρύπανση” (pollution) “Ατμοσφαιρική ρύπανση” (Air pollution) “Ποιότητα αέρα” (Air Quality) “Αιωρούμενα σωματίδια” (Particulates) “Απορρίμματα” (Waste) “Σκουπίδια” (Garbage) “Συγκοινωνιακό” (Transportation) “Θόρυβος” (Noise) “Ηχορύπανση” (Noise pollution)

Results synopsis

Social Mention (social media) General environmental issues: > 1300 Environmental issues in Thessaloniki (Greek search): > 400 Environmental issues in Thessaloniki (English search): < 800

Trending (tweets) General environmental issues: > 6000 Environmental issues in Thessaloniki (Greek search): > 200 Environmental issues in Thessaloniki (English search): < 20

Greek search with Thessaloniki

English search with Thessaloniki

Differentiation of results

Greek search without Thessaloniki

Neutral sentiment

Participation: Contributors & Posts

Time Line of results

Relation of results with similar surveys

Popular environmental problemsAlmost the same with London

Environmental issues based on European Commission

Conclusions & Future work

Possible to investigate the web for participatory sensing reasons Limitations when it come to text mining tools Not accurate enough based on text annotations/keywords Not accurate semantic analysis

Future work: Text mining tool with more capabilities and fewer limitations

Thank You