Text mining in social media for participatory sensing data A dissertation by Georgios Keikoglou SID:

Participatory sensing

Information Analysis  Data Mining  Text 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

 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) > 1300  General environmental issues: > 1300 > 400  Environmental issues in Thessaloniki (Greek search): > 400 < 800  Environmental issues in Thessaloniki (English search): < 800  Trending (tweets) > 6000  General environmental issues: > 6000 > 200  Environmental issues in Thessaloniki (Greek search): > 200 < 20  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 problems Almost 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