1. Can Social Media Analysis
Improve Collective Awareness of
Climate Change?
Dr. Diana Maynard
University of Sheffield, UK
www.decarbonet.eu

2. The Decarbonet Project
● Scientists predict adverse consequences to our climate unless
stronger actions are taken
● Collective awareness about many climate change issues is still
problematic
● We are exposed to vast amounts of conflicting information
● Hard to know what is accurate and relevant
● DecarboNet: “A Decarbonisation Platform for Citizen
Empowerment and Translating Collective Awareness into
Behavioural Change”
● 3-year EU project, started October 2013

3. DecarboNet Objectives
• Raise Individual and Collective
Awareness
• Trigger Behavioural Change and
Foster Social Innovation
• Analyse Behavioural Patterns and
Information Diffusion

5. Social media analysis for climate change
● Do we really know what people mean when they tweet?
● NLP tools for the automatic discovery of new insights, by
automatically extracting information from social media.
● Extracted information can be linked together to form new facts or
to allow new hypotheses to be explored further.
● What arguments for and against man-made causes of climate
change develop in social media?
● What impact does this information have?
● How do people's opinions change over time?
● What kinds of topics are most engaging for social media users?

6. Media Watch for Climate Change

7. How can this be used to raise
awareness of climate change?
● Organisations need to have a better understanding of public perception
of climate change in order to develop campaigns and strategies.
● Our technology helps them to understand what are the opinions on
crucial topics and events
● How are these opinions distributed in relation to demographic
user data?
● How have these opinions evolved?
● Who are the opinion leaders, and what is their impact and
influence?
● Helps them to improve their campaigns, better target them
● Helps improve both the development and marketing of environment-related
tools and technology by better understanding social perception
and behaviour
● Hard to get this information by traditional means (youGov polls etc)

8. We are all connected to each other...
● Information,
thoughts and
opinions are
shared prolifically
on the social web
these days
● 72% of online
adults use social
networking sites

9. Your grandmother is three times as likely to
use a social networking site now as in 2009

10. ● In Britain and the
US, approx 1 hour
a day on social media
● 30 % of Americans get
all their news from
Facebook
● Facebook has more
users than the whole of
the Internet in 2005
● 40% of Twitter users
don't actually tweet
anything, but may still
● be reading

11. But back to your grandmother...
● Does your grandmother care
about climate change?
● The elderly may be more at risk
from climate-related threats due to
an increased likelihood of
deteriorating health
● Life experience and knowledge of
the over 50s means they are
uniquely placed to comment on
government responses to political
and economic crises.

12. Social media
and climate
change are
not just for
young people!

13. Analysing Social Media is harder than it
sounds
There are lots
of things to
think about!

14. Not every tweet about
global warming is
relevant

15. Some tweets are sarcastic
“Climate
change? Oh
great!” says
Justin Bieber.

16. Let's search for keywords like “Arctic”
Oops!

17. Opinion Mining involves finding out what
people think
● A simple approach would look for positive and negative words in a tweet.
● e.g. “Climate change is terrible.” “Recycling is a good way to save the
planet.”
● We could simply collect lists of positive and negative words and
categorise the tweets accordingly.
● But it's not as simple as that.

18. Language in Social Media is complicated
● Grundman:politics makes #climatechange scientific
issue,people don’t like knowitall rational voice tellin em wat 2do
● @adambation Try reading this article , it looks like it would be
really helpful and not obvious at all. http://t.co/mo3vODoX
● Want to solve the problem of #ClimateChange? Just #vote for a
#politician! Poof! Problem gone! #sarcasm #TVP #99%
● 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!

19. Challenges for NLP
● Noisy language: unusual punctuation, capitalisation, spelling,
use of slang, sarcasm etc.
● Terse nature of microposts such as tweets
● Use of hashtags, @mentions etc causes problems for
tokenisation #thisistricky
● Lack of context gives rise to ambiguities
● NER performs poorly on microposts, mainly because of linguistic
pre-processing failure
● Running standard IE tools (ANNIE) on 300 news
articles – 87% F-measure
● Running ANNIE on some tweets - < 40% F-measure
● Other tools (e.g. Stanford NER) can reach even lower
scores

20. Persons in news articles

21. Persons in tweets

22. Lack of context causes ambiguity
Branching out from Lincoln park after dark ...
Hello Russian Navy, it's like the same thing but with glitter!
??

23. Getting the NEs right is crucial
Branching out from Lincoln park after dark ...
Hello Russian Navy, it's like the same thing but with glitter!

24. But there are lots of tools that “analyse”
social media already....
● Streamcrab http://www.streamcrab.com/
● Semantria http://semantria.com
● Social Mention http://socialmention.com/
● Sentiment140: http://www.sentiment140.com/
● TipTop: http://feeltiptop.com/

25. Why are these sites unsuccessful?
● They don't work well at more than a very basic level
● They mainly use dictionary lookup for positive and negative
words
● Or they use ML, which only works for text that's similar in style
to the training data
● Things like sarcasm which occur less frequently may not get
picked up
● They classify the tweets as positive or negative, but not with
respect to the keyword you're searching for
● keyword search just retrieves any tweet mentioning it,
but not necessarily about it as a topic
● no correlation between the keyword and the sentiment

26. “Positive” tweets about fracking
● Help me stop fracking. Sign the petition to David
Cameron for a #frack-free UK now!
● I'll take it as a sign that the gods applaud my new anti-fracking
country love song.
● #Cameron wants to change the law to allow #fracking
under homes without permission. Tell him NO!!!!!

27. It's not just about looking at sentiment words
“It's a great movie if you have the taste and sensibilities of a 5-year-old
boy.”
“I'm not happy that John did so well in the debate last night.”
“I'd have liked the film if it had been shorter.”
“You're an idiot.”
● Conditional statements, content clauses, situational context can
all play a role

28. Whitney Houston wasn't very popular...

29. Death confuses opinion mining tools
 Opinion mining
tools are good for a
general overview,
but not for some
situations

30. Decarbonet Rule-based Opinion Mining
● Although ML applications are typically used for Opinion
Mining, there are advantages to using a rule-based
approach
● No need for large amounts of training data
● Working with multiple languages
● Working on different domains
● Rule-based system is more easily adaptable
● Novel use of language and grammar makes ML hard

31. Text Analysis with GATE
● GATE is a toolkit for Natural Language Processing (NLP)
developed at the Univesity of Sheffield
● http://gate.ac.uk
● components for language processing: parsers, machine learning
tools, stemmers, IR tools, IE components for various languages.
opinion mining
● tools for visualising and manipulating text, annotations,
ontologies, parse trees, etc.
● various information extraction tools
● evaluation and benchmarking tools
● And it's 20 years old next week!

32. GATE Components for Opinion Mining
● TwitIE
● structural and linguistic pre-processing, specific to
Twitter
● includes language detection, hashtag retokenisation,
POS tagging, NER
● Term recognition using TermRaider
● Sentiment gazetteer lookup
● JAPE opinion detection grammars
● Include target and opinion holder detection based on
entities/terms
● currently positive/negative, extending to emotion
detection (happy/sad/anger/fear etc)

33. TwitIE

34. Basic approach for opinion finding
● Find sentiment-containing words in a linguistic relation
with terms/entities (opinion-target matching)
● life flourishing in Antarctica
● Dictionaries give a starting score for sentiment words
● Use a number of linguistic sub-components to deal with
issues such as negatives, adverbial modification, swear
words, conditionals, sarcasm etc.

35. A positive sentiment list
● awesome category=adjective score=0.5
● beaming category=adjective score=0.5
● belonging category=noun score=0.5
● benefic category=adjective score=0.5
● benevolently category=adverb score=0.5
● caring category=noun score=0.5
● charitable category=adjective score=0.5
● charm category=verb score=0.5

36. A negative sentiment list
Examples of phrases following the word “go”:
● down the pan
● down the drain
● to the dogs
● downhill
● pear-shaped

37. Opinion scoring
● Sentiment gazetteers (developed from sentiment words in
WordNet) have a starting “strength” score
● These get modified by context words, e.g. adverbs, swear
words, negatives and so on
amazing campaign --> really amazing campaign.
awful campaign --> absolutely awful. campaign
good campaign --> not so good campaign
● Swear words modifying adjectives count as intensifiers
good campaign --> damned good campaign
● Swear words on their own are classified as negative
Damned politicians.

38. A positive tweet

39. A negative tweet

40. A Sarcastic Tweet

41. Analysis of the EarthHour campaign
● Analysis of hashtags and
topics mentioned
● The main activities and themes
of the campaign drove most of
the social media conversations
● Users engaged in the
campaign but did not
necessarily engage with
climate change and
sustainability issues.
● Lack of correlation between
Durex campaign and climate
change engagement

42. Engagement Analysis
● Retweets as the strongest engagement action
● Identify the characteristics of those tweets that are followed
by
● an engagement action (retweet)
● a high level of engagement (high number of retweets)
● For generating engagement, the content of the tweet is more
relevant than the reputation of the user.
● This contradicts previous findings in other domains
● Posts generating attention are slightly longer, easier to read,
have positive sentiment, mention other users and repeat
terminology from other posts
● People are perhaps bored of hearing doom and gloom about
how the world is going to end?
42

43. Summary
● Tools for social media analysis could be very useful to
companies and organisations involved in climate change
● Understanding engagement and user impact of campaigns,
products etc.
● Also useful to the general public to understand issues better
● While retweets etc are a useful indicator of engagement, the
key is understanding content of social media posts
● For this we need in-depth analysis of many language issues
● AI can help us understand what is really going on!