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EMOTIVE: Data Collection 19 th July, Loughborough University, UK Dr. Tom Jackson, Dr. Ann O’Brien and Dr. Martin Sykora.

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Presentation on theme: "EMOTIVE: Data Collection 19 th July, Loughborough University, UK Dr. Tom Jackson, Dr. Ann O’Brien and Dr. Martin Sykora."— Presentation transcript:

1 EMOTIVE: Data Collection 19 th July, Loughborough University, UK Dr. Tom Jackson, Dr. Ann O’Brien and Dr. Martin Sykora

2 EMOTIVE Work Packages and Milestones

3 Contents Previous Work Technical Considerations Retrieval of Datasets Exploring Taxonomy / Ontology

4 - Guardian (with academic collaboration) work on London 2011 Riots - Crisees Demonstrator - Terrorism Informatics: Cheong and Lee (2011) Previous Work Technical Considerations Retrieval of Datasets Cheong M. and Lee V. C. S., 2011. A microblogging-based approach to terrorism informatics: Exploration and chronicling civilian sentiment and response to terrorism events via Twitter, Journal of Information Systems Frontiers – Springer 13 (1), pp. 45-59

5 - Crisees Demonstrator; Kingston Bridge (Glasgow) – Scottish storms Previous Work Technical Considerations Retrieval of Datasets Maxwell, D. and Raue, S. and Azzopardi, L. and Johnson, C. and Oates, S., 2012. Crisees: Real-time monitoring of social media streams to support crisis management, Advances in Information Retrieval – Springer, pp. 573-575

6 - Terrorism Informatics: Cheong and Lee (2011) Previous Work Technical Considerations Retrieval of Datasets Cheong M. and Lee V. C. S., 2011. A microblogging-based approach to terrorism informatics: Exploration and chronicling civilian sentiment and response to terrorism events via Twitter, Journal of Information Systems Frontiers – Springer 13 (1), pp. 45-59

7 Brief overview of technical challenges: Collecting and processing data in real-time Filtering (spam detection / influential Tweets / Influential Twitter Accounts) and aggregating the datasets Assessing the integrity of the datasets Potential need to store massive datasets (MongoDB) Previous Work Technical Considerations Retrieval of Datasets

8 DATASETS based on UK REGIONS Retrieve geo-tagged tweets only (small minority) …other ways of restricting Tweet retrieval to UK based Tweets Use look-up lists of entities / places / cities / main-roads / etc. in the UK (tricky; King’s Cross station vs. King’s Cross city in Sydney) Complementary Approach; differences in dialects, e.g. Nottingham / Nottinghamshire, Newcastle, Manchester, London,… (identify heuristics & automated classification) Location inferred from the event Previous Work Technical Considerations Retrieval of Datasets

9 DATASETS based on NATIONAL SECURITY RISKS Hashtags # concerning national security  starting point; threats list compiled by Cheong and Lee (2011) Tweet collection operating at all times, collect Tweets before events happen: preselect known events such as the G20 or G8 Also collect datasets relating to emergencies; i.e. floods in UK understanding the “lifecycle” of such an event on Twitter  Sentiment of Terse natural language… Previous Work Technical Considerations Retrieval of Datasets

10 Exploratory Data Collection: M6 Previous Work Technical Considerations Retrieval of Datasets Chris Halpin ‏@KatsJonouchi This M6 toll thing... If you're going to try and blow up a coach, you wouldn't choose a Megabus, surely... Armani Music ‏@ArmaniBmusic Someone was seen pouring a liquid in something that made fumes on a Mega bus on the M6 Toll. I will full on wee myself if it's a pot noodle. Johnny O'Grady ‏@Johnnnnyyyyyyyy Watching the news in Tescos, bit confused about what's happened on the M6 toll road Ellie Henderson ‏@EllieH_86 Distracted at work watching the M6 Toll footage on the news.#M6Toll#M6Toll Ian Watson ‏@IanWatoop M6 Toll. "I picked the wrong day to give up smoking!". #Megabus#Megabus Splashy♥ ❤ ‏@_Splashy_ 2001, terrorists hijack planes. 2012, terrorism alert on a Megabus on the M6 toll. This recession has hit hard. Zora Suleman ‏@ZoraSuleman Police confirm.. the closure of the M6 toll road was caused by an Electric cigarette

11 Exploratory Data Collection: Kings Cross Station Previous Work Technical Considerations Retrieval of Datasets ‏@Pearcesport|17 days before Olympics, and Kings Cross station, one of main gateways to Olympic Park, has been closed this morning due to overcrowding @thefashionturd Got evacuated from Kings cross st pancras! not half as scary as Boris' Olympic messages that are being played into tube stations though... @corrina_kylie Kings Cross is filled with scary characters, I'll say that much. @subhajitb Some pretty angry people outside Kings Cross tube station#fasterhigherstrongerhttp://twitter.com/subhaji tb/status/222599112976105472/photo/1 @MoniqueBabyx RIP to the 18 year old boy who died in kings cross. so sad, so young, so early. prayers go to the innocent family.

12 Taxonomy / Ontology 1 Make a Taxonomy first  This establishes correct relationships  Then create ontology (OWL) Scope 1.Civic unrest? UK? 2.Emotions Range of Language for Ontology 1  ‘normal’ English  Urban slang  Street talk  Text language Exploring Taxonomy / Ontology

13 Vocabulary sources Thorne, T. Dictionary of contemporary slang www.urbandictionary.com www.odps.org www.chavscum.co.uk etc. and from Twitter feeds and archives Exploring Taxonomy / Ontology

14 Taxonomy / Ontology 2 A number of ‘lists’ of emotions exist:  Project Muse  Art and architecture thesaurus  Tate Gallery  Etc. Exploring Taxonomy / Ontology

15 Tate galleries – emotion Exploring Taxonomy / Ontology

16 Linguistic and semantic issues At the extraction stage:  Single terms and noun phrases: ‘police car’, etc. Barriers to understanding  Sarcasm and metaphor Types of relationships between terms:  Synonyms  Broad to narrow (Classes and subclasses) Exploring Taxonomy / Ontology

17 For example… Exploring Taxonomy / Ontology Guns glock piece pistol etc.

18 ‘Guns’ is one subclass of ‘Weapons’ Exploring Taxonomy / Ontology Weapons Glock piece pistol guns knives

19 The issues and approaches discussed in the context of Twitter, also apply to public Facebook messages. Other considerations: - Age and Gender detection from Terse natural language - Scope of Communication (social network analysis) - Followers of certain bands / parties (maybe extreme; neo-nazi, leftist, etc.) Final Remarks…

20 Thanks


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