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Understanding the world with NLP: interactions between society, behaviour and social media
The document discusses analyzing social media data, particularly tweets, for natural language processing tasks. It provides examples of analyzing tweets to understand information sharing during disasters, monitor opinions in real-time, detect topics and analyze political discussions. It also discusses challenges in analyzing tweets like informal language, ambiguity and misleading contexts or hashtags. Precise information extraction and annotation of tweets is needed to accurately identify hate speech, abuse and analyze its targets and changes over time. A multi-step pipeline including collection, preprocessing, information extraction and classification is proposed to understand abuse toward politicians from tweets surrounding UK elections.
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Understanding the world with NLP: interactions between society, behaviour and social media
- 1. Understanding the worldwith NLP: interactions between society, behaviour and social media Dr. Diana Maynard University of Sheffield, UK 27 February 2019, POLIMI, Milan
- 2. Twitter Fun Facts Howmany tweets are sent per day worldwide?
- 3. Twitter Fun Facts 500million tweets sent per day
- 4. Twitter Fun Facts Howmany twitter users worldwide?
- 5. Twitter Fun Facts In2018, more than 336 million monthly active users worldwide
- 6. Who uses Twittermore: men or women?
- 7. Men or women? •24% of all internet male users use Twitter • 21% of all internet female users use Twitter Out of all Twitter users (in 2019): • 34.5% are women • 65.5% are men
- 8. How old aretwitter users? • Out of all twitter users, which age range has the higher percentage?
- 9. Older or younger? Inthe USA in 2018
- 10. Which country hasthe most Twitter users?
- 11. Twitter Users percountry • US: 67 million • Brazil: 27.7 million • Japan: 25.9 million • Mexico: 23.5 million .... • UK: 13 million
- 12. Which country hasthe highest penetration of Twitter users? (percentage of Twitter users out of those who use the Internet)
- 14. 1/3 of all internet usersthere are on Twitter
- 15. Who do wefollow on Twitter?
- 16. Top 10 mostfollowed Twitter users 2017 2015 2013 Katy Perry Katy Perry Katy Perry Justin Bieber Justin Bieber Justin Bieber Barack Obama Taylor Swift Lady Gaga Taylor Swift Barack Obama Barack Obama Rihanna Youtube Taylor Swift Ellen de Generes Lady Gaga YouTube Lady Gaga Rihanna Britney Spears Youtube Ellen de Generes Rihanna Justin Timberlake Twitter Instagram Twitter Justin Timberlake Justin Timberlake
- 17. Social media: avaluable source of information (not just stupid stuff about pop stars) l business insights l sharing and receiving news l campaigns l all kinds of collective intelligence l an alternative to traditional polls l and much more
- 18. Why is socialmedia interesting for NLP? l Fast-growing, highly dynamic and high volume source of data – big data! l Reflects language used in today's society l Reflects current views of society l It's a great source of material for opinion mining tools l Challenging research area due to specialised use of language
- 19. Gartner 3V definitionof Big Data • Volume • Velocity • Variety • +Veracity • High volume & velocity of messages: • 500 million tweets per day • Massive variety: • Stock markets • Earthquakes • Social arrangements
- 20. Big Data isnot new! Staff sorting 4M used tickets from #London Underground to analyse line use in 1939
- 21. Linguistic challenges ofsocial media • Language • Problem: typically exhibits very different language style • Solution: train specific language processing components • Relevance • Problem: topics and comments can rapidly diverge. • Solution: train a classifier or use clustering techniques • Lack of context • Problem: hard to disambiguate entities • Solution: data aggregation, metadata, entity linking
- 22. People don’t write“properly” l Grundman:politics makes #climatechange scientific issue,people don’t like knowitall rational voice tellin em wat 2do l Want to solve the problem of #ClimateChange? Just #vote for a #politician! Poof! Problem gone! #sarcasm #TVP #99% l Human Caused #ClimateChange is a Monumental Scam! http://www.youtube.com/watch?v=LiX792kNQeE … F**k yes!! Lying to us like MOFO's Tax The Air We Breath! F**k Them! l The last people I will listen2 about guns r those that know nothing about them&politicians who live in states w/strictest gun laws #cali #ny
- 23. NLP Pre-Processing Pipeline LanguageID TokenisationPOS tagging Text
- 24. Named Entity Recognitionand Linking NER (Professor Plum) dbpedia.org/resource/..... Michael_Jackson Michael_Jackson_(writer) Entity Linking
- 25. Pipelines for tweets Errorshave a cumulative effect Good performance is important at each stage Per-stage Overall
- 26. Understanding tweets ishard even for people Branching out from Lincoln park after dark ... Hello Russian Navy, it's like the same thing but with glitter! ??
- 27. It’s all aboutthe Named Entities Branching out from Lincoln park after dark ... Hello Russian Navy, it's like the same thing but with glitter!
- 28. Some cool applicationswith GATE
- 29. 2 9 Ecuador, 7.8 earthquake, April 2017, ~700 people die Droughts, affecting 60 million in 34 countries Maxwell, California, Feb 2017 Portugal, forest fires, 64 confirmed deaths, Jun 2017 Manchester, May 2017, 22 dead Haiti, Hurricane Matthew, Oct 2016, ~500 people died, farming devastated
- 30.
- 31. Uses of socialmedia during disasters • Broadcasting info about the disaster • Requesting info from local people and eyewitnesses • Requesting and offering help and support • Disaster mapping • Mobilising the crowd to support initiatives
- 32. How? • Using citizen reporters,and digital responders for mapping crises • Ushahidi deployed over 50k times • Free and open source • Working with us on the COMRADES project
- 33. Categorising for: • relevance •informativeness • actionability • veracity
- 34. Understanding climate change: sexwill save the planet!
- 36. Behaviour Analysis • Basedon the assumption that users in different behavioural stages communicate differently (different emotions, directives, etc.) Pajarito @lindopajarito . 2h Our building needs 40% of all energy consumed in Switzerland! L DJPajarito @DJPajaritoGenial . 12h I'm so proud when I remember to save energy and I know however small it's helping. Desirability: Negative sentiment (expressing personal frustration- anger/sadness) Buzz: Positive sentiment (happiness/joy). I/we + present tense HotelPajarito @HotelPajarito . 18h Join us today today to switch of a light for EH! J Invitation: Positive sentiment (happy) + use of vocatives
- 37. • How dopeople talk about elections and political events? • How do the MPs talk about different topics? • How does the public respond to them? Social media and politics
- 38.
- 39. Climate change, ISISand Trump 39
- 40. Parties / themesco-occurrence UK economy Europe Tax and revenue NHS Borders and Immigration Scotland Employment Community and society Public health Media and communications LabourPartyCandidate LabourPartyMP ConservativePartyCandidate ConservativePartyMP UKIPCandidate OtherMP SNPCandidate GreenPartyCandidate LiberalDemocratsCandidate SNPOther
- 41. Twits, twats andtwaddle: analysis of hate speech towards politicians
- 42. Online abuse • Putspeople off debating online • Puts people off becoming politicians • Seems to be getting worse • Might be particularly bad for particular groups (females, ethnic minorities, LGBT etc) "I am seriously considering whether or not to stand next time" "My staff try not to let me go out alone" "Misogynist comments, sexual abuse … My children saw this" "death threats"
- 43. • Let’s findall the “hate” terms in the messages and look at who they’re directed at • Then we can see who gets the most, and how this changes over time • Plug in a swear word lexicon, do a bit of number crunching, and then head off to a desert island for a nice holiday How do we identify hate speech?
- 44. What could possiblygo wrong with this plan?
- 45. Swear words area sign of abuse, right?
- 46. Well, maybe notalways
- 47. What about ifwe specifically mention someone with a nasty word? That has to be bad, right? You *$!%*&”!
- 48.
- 49. Hashtags can bemisleading • These are all perfectly innocent: #powergenitalia #lesbocages #molestationnursery #teacherstalking #therapist
- 50. And what aboutforeign words? #slagroom
- 51. But we stillneed to analyse hashtags
- 52. • Who isbeing abused? • Who is abusing them? • What is the abuse about? • Is it really getting worse? Aims of the Analysis
- 53. • Collect tweetsto and from politicians in the run-up to the 2015 and 2017 UK elections • Annotate all the interesting information (who, what, when, where) with the social media toolkit • Run an abuse classifier • Analyse the results Plan
- 54.
- 57. Tweets Tokenization Normalizatio n POS tagging NE recognition Leave/remai n classification Topic Detection Tweet Geolocation User classification Semantic Search Tweetsare tracked in real time using the streaming API Tweet Collection
- 58.
- 59. Tweets Tokenization Normalization POS tagging NE recognition Leave/remai n classification Topic Detection Tweet Geolocation User classification Semantic Search Spellingand abbreviations are normalised Normalization
- 60.
- 61. Tweets Tokenization Normalization POS tagging NE recognition Leave/remai n classification Topic Detection Tweet Geolocation User classification Semantic Search Wediscover mentions of entities such as people, locations, organisations and products Named Entities
- 62. Tweets Tokenization Normalization POS tagging NE recognition MP recognition Topic Detection Tweet Geolocation User classification Semantic Search Findmentions of MPs and link to information from YourNextMP + DBpedia Politician Recognition
- 63. Tweets Tokenization Normalization POS tagging NE recognition MP recognition Topic Detection Tweet Geolocation User classification Semantic Search Tweetsare matched against a detailed topic ontology Topic Detection
- 64. Tweets Tokenization Normalization POS tagging NE recognition MP recognition Topic Detection Tweet Geolocation User classification Semantic Search Tweetsare linked to NUTS regions based on place tags and user home locations Geolocation
- 65. Tweets Tokenization Normalization POS tagging NE recognition MP recognition Topic Detection Tweet Geolocation User classification Semantic Search Weclassify users into e.g. journalist, charity, member of public User Classification
- 66. Tweets Tokenization Normalization POS tagging NE recognition Leave/remai n classification Topic Detection Tweet Geolocation User classification Semantic Search Tweettext and annotations are indexed in semantic search engine Mímir for search and visualisation Semantic Search
- 67. Finding abusive terms nigger witch homo Godbotherer • 404 abusive terms collected • But only annotated when used in specific situations shut up f**k you Uncivil language idiot kill Threats die Obscene nouns cunt twat rape Racist and bigoted language
- 68.
- 69. Did the abuseget worse? There was more abuse in 2017 than in 2015 20172015
- 70. Who got theabuse in 2015? • Men got more abuse then women • Conservatives got more abuse than Labour
- 71. Who got theabuse in 2015? A small number of prominent MPs
- 72. What about in2017? The same thing happened (but to different people)
- 73. Check out theinteractive version! http://demos.gate.ac.uk/politics/buzzfeed/sunburst.html
- 74. Let’s look abit deeper • Did men get more abuse because more men are Conservatives? • Did more prominent people get more abuse just because they got more tweets? • Structural equation modelling using the Lavaan package in R
- 75. • Analysing socialmedia is super interesting • But super tricky (even for humans) • Need good underlying language analysis tailored to social media, before attempting anything else • Remember that correlation ≠ causation Conclusions
- 76. The advertising bit •Come on a GATE training course! • 17-21 June in Sheffield • https://gate.ac.uk/conferences/fig/fig12.html • Come on a research visit to us! • SoBigData Transnational Access Program • Fully funded research visits of 2-8 weeks to work with us on a social media analysis project • http://sobigdata.eu/content/open-call-sobigdata-funded- transnational-access
- 77. Some useful links •GATE http://gate.ac.uk • GateCloud https://cloud.gate.ac.uk • Come on a GATE training course! • Blog posts about our GATE social media work http://gate4ugc.blogspot.co.uk/search/label/Brexit • UK elections monitor http://gate.ac.uk/projects/pft • COMRADES project on social media during disasters http://gate.ac.uk/projects/comrades • WeVerify project looking at rumour detection http://weverify.eu
- 78. Publications • Diana Maynard,Ian Roberts, Mark A. Greenwood, Dominic Rout and Kalina Bontcheva. A Framework for Real-time Semantic Social Media Analysis. Web Semantics: Science, Services and Agents on the World Wide Web, 2017. • G. Gorrell, M. Greenwood, I. Roberts, D. Maynard, K. Bontcheva. Twits, Twats and Twaddle: Trends in Online Abuse towards UK Politicians. In Proceedings of the 12th International Conference on Web and Social Media (ICWSM 2018), 25-28 June 2018, Stanford, US • Diana Maynard, Kalina Bontcheva, Isabelle Augenstein. Natural Language Processing for the Semantic Web. Morgan and Claypool, December 2016. ISBN: 9781627059091 (contains a chapter on social media analysis) • M. Fernandez, L. Piccolo, D. Maynard, M. Wippoo, C. Meili, H. Alani. Pro- Environmental Campaigns via Social Media: Analysing Awareness and Behaviour Patterns. Journal of Web Science 2017. • G. Resce and D. Maynard. What matters most to people around the world? Retrieving Better Life Index priorities on Twitter. Journal of Technological Forecasting & Social Change 2018. • Available (and more) at https://gate.ac.uk/gate/doc/papers.html
- 79. Acknowledgements This work supportedby: • the European Union/EU under the Information and Communication Technologies (ICT) theme of the 7th Framework and H2020 Programmes for R&D • DecarboNet (610829) http://www.decarbonet.eu ● SoBigData (654024) http://www.sobigdata.eu ● COMRADES (687847) http://www.comrades-project.eu ● WeVerify (825297) https://weverify.eu/ ● EPSRC grant EP/I004327/1 and British Academy under call “The Humanities and Social Sciences: Tackling the UK’s International Challenges”. ● Nesta http://nesta.org.uk