can social media tell us something about our lives?
TRANSCRIPT
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Can Social Media tell us something
about our lives?
Vasileios Lampos
Computer Science DepartmentUniversity of Sheffield
March, 2013
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Outline
Motivation, Aims [Facts, Questions] Data
Nowcasting Events
Extracting Mood Patterns
TrendMiner Extracting Political Opinion
|= Conclusions
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Facts
We started to work on those ideas back in 2008, when...
Web contained 1 trillion unique pages (Google)
Social Networks were rising, e.g. Facebook: 100m (2008) >1 billion active users (October, 2012) Twitter: 6m (2008) 500m active users (July, 2012)
User behaviour was changing
Socialising via the Web Giving up privacy (Debatin et al., 2009)
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Some general questions
Does user generated text posted on Social Web platforms include
useful information?
How can we extract this useful information...... automatically? Therefore, not we, but a machine.
Practical / real-life applications?
Can those large samples of human input assist studies in otherscientific fields?Social Sciences, Psychology, Epidemiology
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The Data (1/3)
Why Twitter?
Has a lot of content that is publicly accessible Provides a well-documented API for several types of data collection
Opinions and personal statements on various domains
Connection with current affairs (usually in real-time) Some content is geo-located
Option for personalised modelling ... and we got good results from the very first, simple experiment!
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The Data (2/3)What does a @tweet look like?
Figure 1 : Some biased and anonymised examples of tweets (limit of 140characters/tweet, # denotes a topic)
(a) (user will remain anonymous) (b) they live around us
(c) citizen journalism (d) flu attitude
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The Data (3/3)
Data Collection & Preprocessing
The easiest part of the process... not true! Storage space, crawler implementation, parallel dataprocessing, new technologies (e.g., Map-Reduce) (Preotiuc et al., 2012)
Data collected via Twitters Search API:
collective sampling tweets geo-located in 54 urban centres in the UK periodical crawling (every 3 or 5 minutes per urban centre)
Data collected via Twitters REST API:
user-centric sampling
preprocessing to approximate users location (city & country) ... or manual user selection from domain experts get their latest tweets (3,000 or more)
Several forms of ground truth (flu/rainfall rates, polls)
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Nowcasting Events from the
Social Web
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Nowcasting?
We do not predict the future, but infer the present i.e. the very recent past
Figure 2 : Nowcasting the magnitude of an event () emerging in the real worldfrom Web information
Our case studies: nowcasting (a) flu rates & (b) rainfall rates (?!)
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What do we get in the end?
This is a regression problem (text regression in NLP)i.e. time interval i we aim to infer yi R using text input xxxi Rn
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16
Days
R
ainfallr
ate(m
m)
Bristol
Actual
Inferred
Figure 3 : Inferred rainfall rates for Bristol, UK (October, 2009)
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M h d l (1/5) T i V S
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Methodology (1/5) Text in Vector Space
Candidate features (n-grams): C = {ci}Set of Twitter posts for a time interval u:
P(u) =
{pj
}Frequency of ci in pj:
g(ci, pj) =
if ci pj,0 otherwise.
g Boolean, maximum value for is 1
Score of ci in P(u):
s
ci, P(u)
=
|P(u)
|j=1
g(ci, pj)
|P(u)|
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M h d l (2/5)
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Methodology (2/5)
Set of time intervals: U= {uk} 1 hour, 1 day, ...
Time series of candidate features scores:
X(U) =
xxx(u1) ... xxx(u|U|)T
,
wherexxx(ui) =
s
c1, P(ui)
... s
c|C|, P(ui)T
Target variable (event):
yyy(U) =
y1 ... y|U|T
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M th d l (3/5) F t l ti
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Methodology (3/5) Feature selection
Solve the following optimisation problem:
minw
X(U)www
yyy(U)
22
s.t. www1 t,t = wwwOLS1 , (0, 1].
Least Absolute Shrinkage and Selection Operator (LASSO)argmin
wwwX(U)www yyy(U)22 + www1
(Tibshirani, 1996)
Expect a sparse www (feature selection)
Least Angle Regression (LARS) computes entire regularisationpath (wwws for different values of ) (Efron et al., 2004)
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M th d l (4/5)
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Methodology (4/5)
LASSO is model-inconsistent:
inferred sparsity pattern may deviate from the true model, e.g.,when predictors are highly correlated (Zhao and Yu, 2006)
bootstrap [?] LASSO (Bolasso) performs a more robust featureselection (Bach, 2008)?:
in each bootstrap, input space is sampled with replacement apply LASSO (LARS) to select features select features with nonzero weights in all bootstraps
better alternative soft-Bolasso: a less strict feature selection select features with nonzero weights in p% of bootstraps (learn p using a separate validation set)
weights of selected features determined via OLS regression
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Methodolog (5/5) Sim lified s mma
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Methodology (5/5) Simplified summary
Observations: X
R
mn (m time intervals, n features)
Response variable: yyy Rm
For i = 1 to number of bootstrapsForm Xi
X by sampling X with replacement
Solve LASSO for Xi and yyy, i.e. learn wwwi RnGet the k n features with nonzero weights
End_For
Select the v n features with nonzero weight in p% of the bootstrapsLearn their weights with OLS regression on X(v) Rmv and yyy
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How do we form candidate features?
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How do we form candidate features?
Commonly formed by indexing the entire corpus(Manning, Raghavan and Schtze, 2008)
We extract them from Wikipedia, Google Search results, PublicAuthority websites (e.g., NHS)
Why? reduce dimensionality to bound the error of LASSO
L(www) L(www) + Q, with Q min
W21N
+p
N,
W21N
+W1
N
p candidate features, N samples, empirical loss
L(www) and
www1 W1 (Bartlett, Mendelson and Neeman, 2011)
Harry Potter Effect!
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The Harry Potter effect (1/2)
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The Harry Potter effect (1/2)
Figure 4 : Events co-occurring (correlated) with the inference target may affectfeature selection, especially when the sample size is small.
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100
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300
Day Number (2009)
Event
Score
Flu (England & Wales)
Hypothetical Event I
Hypothetical Event II
(Lampos, 2012a)
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The Harry Potter effect (2/2)
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The Harry Potter effect (2/2)Table 1 : Top 1-grams correlated with flu rates in England/Wales (0612/2009)
1-gram Event Corr. Coef.latitud Latitude Festival 0.9367
flu Flu epidemic 0.9344swine 0.9212harri Harry Potter Movie 0.9112
slytherin 0.9094potter 0.8972
benicassim Benicssim Festival 0.8966graduat Graduation (?) 0.8965
dumbledor Harry Potter Movie 0.8870hogwart 0.8852quarantin Flu epidemic 0.8822gryffindor Harry Potter Movie 0.8813ravenclaw 0.8738
princ 0.8635swineflu Flu epidemic 0.8633
ginni Harry Potter Movie 0.8620weaslei 0.8581
hermion 0.8540draco 0.8533
Solution: ground truth with some degree of variability
(Lampos, 2012a)
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About n grams
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About n-grams
1-grams
decent (dense) representation in the Twitter corpus unclear semantic interpretation
Example: I am not sick. But I dont feel great either!
2-grams
very sparse representation in tweets
sometimes clearer semantic interpretation
Experimental process indicated that...
a hybrid combination of 1-grams and 2-grams
delivers the best inference performance
refer to (Lampos, 2012a)
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Flu rates Example of selected features
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Flu rates Example of selected features
Figure 5 : Font size is proportional to the weight of each feature; flipped n-gramsare negatively weighted. All words are stemmed (Porter, 1980).
(Lampos and Cristianini, 2012)
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Rainfall rates Example of selected features
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Rainfall rates Example of selected features
Figure 6 : Font size is proportional to the weight of each feature; flipped n-gramsare negatively weighted. All words are stemmed (Porter, 1980).
(Lampos and Cristianini, 2012)
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Examples of inferences
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Examples of inferences
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20
40
60
80
100
120
Days
FluRate
C.En
gland&Wales
Actual
Inferred
(a) Central England/Wales (flu)
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20
40
60
80
100
120
Days
FluRate
S.England
Actual
Inferred
(b) South England (flu)
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16
Days
R
ainfallrate(mm)
Bristol
Actual
Inferred
(c) Bristol (rain)
Figure 7 : Examples of flu and rainfall rates inferences from Twitter content
(Lampos and Cristianini, 2012)
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Performance figures
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Performance figures
Table 2 : RMSE for flu rates inference (5-fold cross validation), 50m tweets,21/06/200919/04/2010
Method 1-grams 2-grams Hybrid
Baseline 12.442.37 13.813.29 11.621.58
Bolasso 11.142.35 12.642.57 10.572.2
CART ensemble 9.635.21 13.134.72 9.44.21
Table 3 : RMSE (in mm) for rainfall rates inference (6-fold cross validation),8.5m tweets, 01/07/200930/06/2010
Method 1-grams 2-grams Hybrid
Baseline 2.910.6 3.10.57 4.392.99
Bolasso 2.730.65 2.950.55 2.600.68CART ensemble 2.710.69 2.720.72 2.640.63
As implemented in (Ginsberg et al., 2009) Classification and Regression Tree (Breiman et al., 1984) & (Sutton, 2005)
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Flu Detector
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Flu Detector
URL: http://geopatterns.enm.bris.ac.uk/epidemics
Figure 8 : Flu Detector uses the content of Twitter to nowcast flu rates inseveral UK regions
(Lampos, De Bie and Cristianini, 2010)
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http://geopatterns.enm.bris.ac.uk/epidemicshttp://geopatterns.enm.bris.ac.uk/epidemics -
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Computing a mood score
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p gTable 4 : Mood terms from WordNet Affect
Fear Sadness Joy Anger
afraid depressed admire angryfearful discouraged cheerful despise
frighten disheartened enjoy enviouslyhorrible dysphoria enthousiastic harassed
panic gloomy exciting irritate... ... ... ...
(92 terms) (115 terms) (224 terms) (146 terms)
Mood score computation for a time interval d using n mood terms
msd =1
n
ni=1
c(td)iN(td)
c(td)i : count of term i in the Twitter corpus of day d
N(td): number of tweets for day dUsing the sample of d days, compute a standardised mood score:
msstdd =msd ms
ms
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The mood of the nation (1/5)
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( / )
Figure 9 : Daily time series (actual & their 14-point moving average) for themood of Joy based on Twitter content geo-located in the UK
Jul 09 Jan 10 Jul 10 Jan 11 Jul 11 Jan 122
0
2
4
6
8
10
933 Day Time Series for Joy in Twitter Content
Date
NormalisedEmotionalValence
* RIOTS
* CUTS
* XMAS* XMAS
* XMAS
* roy.wed.
* halloween
* halloween
* halloween
* valentine* valentine
* easter
* easter
raw joy signal
14day smoothed joy
(Lansdall, Lampos and Cristianini, 2012a&b)
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The mood of the nation (2/5)
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( / )
Figure 10 : Daily time series (actual & their 14-point moving average) for themood of Anger based on Twitter content geo-located in the UK
Jul 09 Jan 10 Jul 10 Jan 11 Jul 11 Jan 124
3
2
1
0
1
2
3
4
5933 Day Time Series for Anger in Twitter Content
Date
NormalisedEmotionalValence
* RIOTS
* CUTS
* XMAS
* XMAS
* XMAS
* roy.wed.
* halloween
* halloween
* halloween
* valentine
* valentine* easter
* easter
raw anger signal
14day smoothed anger
(Lansdall, Lampos and Cristianini, 2012a&b)
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The mood of the nation (4/5)
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( / )
Figure 12 : Projections of 4-dimensional mood score signals (joy, sadness, anger andfear) on their top-2 principal components (PCA) Twitter content from 2011
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1st Principal Component
2ndPrincipalComponent
Days of the Week
(a) Days of the week (2011)
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1st Principal Component
2ndPrincipalComponent
Days in 2011
(b) Days of the year (2011)
Cluster INew Year (1), Valentines (45), Christmas Eve (358), New Years Eve (365)
Cluster IIO.B. Ladens death (122), Winehouses death + Breivik (204), UK riots (221)
(Lampos, 2012a)
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The mood of the nation (5/5)
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URL: http://geopatterns.enm.bris.ac.uk/mood
Figure 13 : Mood of the Nation uses the content of Twitter to nowcast moodrates in several UK regions
(Lampos, 2012a)
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Circadian mood patterns (1/3)
http://geopatterns.enm.bris.ac.uk/moodhttp://geopatterns.enm.bris.ac.uk/mood -
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Compute 24-h mood score patterns
Mood score computation for a time interval u = 24hours using nmood terms (WordNet) and a sample of D days:
Ms(u) = 1|D||D|
j=1
1n
ni=1
sf(tj,
u)i
sf(td,u)
i =f(td,u)
i fif
i
, i {1,..., n}.
f(td,u)
i : normalised frequency of a mood term i during time interval u in day dD
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Circadian mood patterns (2/3)
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Figure 14 : Circadian (24-hour) mood patterns based on UK Twitter content
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Circadian mood patterns (3/3)
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Figure 15 : Autocorrelation of circadian mood patterns based on hourly lagsrevealing daily and weekly periodicities
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0
0.2
0.4
Autocorr. Lags (Hours)
Autocorr.
(Fea
r)
Autocorr.
Conf. Bound
(a) Fear
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0
0.1
0.2
0.3
0.4
Autocorr. Lags (Hours)
Autocorr.
(Sadness)
Autocorr.
Conf. Bound
(b) Sadness
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0.2
0
0.2
0.4
Autocorr. Lags (Hours)
Autocorr.
(Joy)
Autocorr.
Conf. Bound
(c) Joy
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0
0.1
0.2
0.3
Autocorr. Lags (Hours)
Autocorr.
(Anger)
Autocorr.
Conf. Bound
(d) Anger
... further analysis on those patterns (in collab. with domain experts) under submission
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TrendMiner Project
Extracting political opinion fromSocial Media
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A few words about the project...
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TrendMiner is an EU-FP7 project
Several participants incl. the Univ. of Sheffield & Southampton(UK) and DFKI (Germany)
Aims to form methods for interpreting the vast stream of onlineinformation
Our focus on analysis of Twitter content
political opinion,
financial indicators
Work in progress and under submission process cannot go intomuch detail!
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Some new challenges
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Aim: model voting intention regression task multiple outputs
Overcome limitations of previous methods use of sentiment analysis taxonomies language specific, restrictive combined modelling of word frequencies and the domain of users? multi-task learning exploit correlations in the feature space multi-task & multi-domain learning
model political opinion + financial indicators jointly
Proper evaluation k-fold cross-validation may sometimes be misleading can we actually predict future values?
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A snapshot of the results
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vi = uuuTXwww +
(plus multi-task learning)
Figure 16 : 50 voting intention polls (YouGov) and their respective inferred values forthe Conservative (RMSE: 1.781.781.78%), Labour (1.591.591.59%) and Liberal Democrat (1.051.051.05%) parties(Nov. 2011 to Feb. 2012)
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0
5
10
15
20
25
30
35
40
VotingIntention%
Time
CON
LAB
LIB
(a) Voting intention polls
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0
5
10
15
20
25
30
35
40
VotingIntention%
Time
CON
LAB
LIB
(b) Voting intention inferences
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Qualitative evaluation is also essential...
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Some domains may be represented by smooth trends (e.g.,political domain)
Predictions could be easy in that context how do we know we are not overfitting?
Perform qualitative analysis using the selected features (words,users and tweets) Do the selected words and users make some sense? Does their combination make sense? score single tweets
Possibly better models when increasing the statistical evidence(multi-task learning)
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Conclusions Did they tell us anything?
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Social Media hold valuable information
We can develop methods to extract portions of this informationautomatically detect, quantify, nowcast events
extract collective mood patterns
model other domains (such as politics)
User generated input + other features tell/reveal something about the users & their context
Side effect: what about our privacy? ...
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In collaboration with...
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Prof. Nello Cristianini, University of Bristol (Ph.D. Advisor)
Prof. Ricardo Araya, University of Bristol (Psychiatry)
Dr. Tijl De Bie, University of Bristol
Thomas Lansdall-Welfare, University of Bristol
Dr. Trevor Cohn, University of Sheffield (TrendMiner)
Daniel Preotiuc-Pietro, University of Sheffield (TrendMiner)
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Last Slide!
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The end.Any questions?
Download the slides fromhttp://www.lampos.net/research/presentations-and-posters
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References1 B Debatin J P Lovejoy A M A Horn and B N Hughes Facebook and Online Privacy: Attitudes Behaviors
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1. B. Debatin, J.P. Lovejoy, A.M.A. Horn and B.N. Hughes. Facebook and Online Privacy: Attitudes, Behaviors,and Unintended Consequences. Journal of Computer-Mediated Communication 15, pp. 83108, 2009.
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9. V. Lampos and N. Cristianini. Tracking the flu pandemic by monitoring the Social Web. Proceedings of CIP 10,pp. 411416, 2010.
10. V. Lampos, T. De Bie and N. Cristianini. Flu Detector Tracking Epidemics on Twitter. Proceedings of ECMLPKDD 10, pp. 599602, 2010.
11. T. Lansdall-Welfare, V. Lampos and N. Cristianini. Effects of the Recession on Public Mood in the UK.
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2628, 2012.(b)
13. V. Lampos. Detecting Events and Patterns in Large-Scale User Generated Textual Streams with StatisticalLearning Methods. PhD Thesis, University of Bristol, p. 243, 2012.(a)
14. V. Lampos. On voting intentions inference from Twitter content: a case study on UK 2010 General Election .CoRR, 2012.(b)
V. Lampos [email protected] Can Social Media tell us something about our lives? 43/4343/43