∎

11institutetext: L. Pollacci, A. Sîrbu, D. Pedreschi22institutetext:
Department of Computer Science, University of Pisa
Largo B. Pontecorvo 3, 56127 Pisa, Italy
22email<EMAIL_ADDRESS><EMAIL_ADDRESS>33institutetext:
F. Giannotti 44institutetext: Scuola Normale Superiore, P.za dei Cavalieri, 7,
56126 Pisa PI
44email<EMAIL_ADDRESS>
# Measuring the _Salad Bowl_ : Superdiversity on Twitter ††thanks: This work
has been funded by the Horizon2020 European projects “SoBigData Research
Infrastructure - Big Data and Social Mining Ecosystem” (grant agreement
654024), and “HumMingBird – Enhanced migration measures from a
multidimensional perspective” (grant agreement no 871042).

Laura Pollacci  Alina Sîrbu  Fosca Giannotti  Dino Pedreschi

###### Abstract

Superdiversity refers to large cultural diversity in a population due to
immigration. In this paper we introduce a _superdiversity index_ based on the
changes in the emotional content of words used by a multi-cultural community,
compared to the standard language. To compute our index we use Twitter data
and we develop an algorithm to extend a dictionary for lexicon based sentiment
analysis. We validate our index by comparing it with official immigration
statistics available from the European Commission’s Joint Research Center,
through the D4I data challenge. We show that, in general, our measure
correlates with immigration rates, at various geographical resolutions. Our
method produces very good results across languages, being tested here both on
English and Italian tweets. We argue that our index has predictive power in
regions where exact data on immigration is not available, paving the way for a
nowcasting model of immigration rates.

###### Keywords:

Superdiversity Twitter Sentiment AnalysisImmigration Migration Stocks

##  1 Introduction

Superdiversity is a relatively new term that refers to a major cultural
diversity in the population due to recent migration phenomena
vertovec2007super . Superdiversity is difficult to measure in a population. In
terms of migration, one can measure diversity based on the languages spoken in
a country moise2016tracking , immigrant percentage, and other criteria,
however to our knowledge there is no established superdiversity index. At the
same time, there is a very close interplay between diversity, migrant
integration, migrant attachment to the home country, making the measurement of
diversity and superdiversity far from straightforward.

In this work we propose a novel measure of superdiversity that we call the
Superdiversity Index ($SI$). This is based on the emotional content of words
in a community. Persons with different cultural backgrounds will necessarily
associate different emotional valences to the same word. Therefore, a multi-
cultural community will display a use of the local language that is different
in its emotional content compared to a standard expected use. We believe that
a more diverse community has a larger distance between standard and actual
emotional valences.

Our $SI$ is built on Twitter data and lexicon based sentiment analysis.
Specifically, we introduce an algorithm able to calculate emotional valences
for words used on Twitter by various communities, that we apply to the local
language. We then compare the calculated valences with emotional valences from
a standard tagged lexicon. The distance between the two gives a measure of
diversity. We compute our $SI$ at different geographical resolutions, for the
United Kingdom (UK) and Italy, and we show that $SI$ values correlate well
with foreign immigration rates. Furthermore, our $SI$ outperforms by far other
possible measures of (super)diversity extracted from the same Twitter data,
such as the use of multiple languages or lexical richness.

An important characteristic of our $SI$ is the high correlation with
immigration rates. This prompts us to expect that it can become an important
feature in a nowcasting model of migration stocks. Migration flows and stocks
are typically measured at national level by official statistics offices, for
example through regular population censuses. The same applies for other
effects such as social and economic integration. These data are critical for
development of policies to optimise the beneficial effects of migration under
all criteria. However, due to their nature, they can become outdated (censuses
are organised every ten years), or information can be inconsistent when moving
from one national statistics office to another. Hence, lately, alternative
data including social media data are starting to be proposed to measure
migration effects in various settings zagheni2012you ; sirbu2019 ;
kim2020digital . Our work is a first step towards nowcasting immigration from
Twitter data.

The rest of this paper is organised as follows. Section 2 introduces $SI$, a
novel superdiversity index, including a high-level description of the
algorithm used to estimate emotional valences of words used by a community.
Section 3 presents the lexical resources employed to estimate emotional
valences of words. A detailed description of the algorithm used to estimate
emotional valences, the Emotional Spreading Algorithm (EmoSA), is provided in
Section 4. We describe the datasets used for the analysis and the
preprocessing steps in Section 3.2 and 3.3, while in Section 5.1 we evaluate
the emotional valences obtained with EmoSA. The detailed description of the
procedure to compute the $SI$, and the values obtained are shown in Section 6,
and compared with immigration rates. Section 7 discusses the possible
application of this work for nowcasting immigration rates, while Section 9
concludes the paper, after presenting the related work in Section 8.

##  2 Introducing the Superdiversity Index

We start from the hypothesis that different cultures associate different
emotional valences to the same word. So, a culturally diverse community will
show a use of the language that is different from a standard expected use.
Thus, we defined our Superdiversity Index ($SI$) as the _distance_ between the
_community-dependent_ and the _standard_ emotional valences for a set of
words.

Figure 1: Overall process to compute SI.

The overall process to calculate $SI$ for a certain community is summarised in
Figure 1. The first step is to compute the community-dependent emotional
lexicon (CD EmoLex), i.e. a list of words labelled with an emotional polarity
value determined by the community. This is obtained by applying the Emotional
Spreading Algorithm (EmoSA) (see Section 4 and pollacci2017sentiment  for
details). The algorithm takes as input a user-generated content (UCG) dataset,
in our case geolocalised tweets, and a collection of existing labelled
emotional lexicons. The CD EmoLex returned contains emotional values that
refer to the specific emotional usage of words by the community of users
posting on Twitter from the same geographical area.

The emotional valences in the CD EmoLex are compared to the standard emotional
valences from an established lexicon. The reliability of the standard
emotional lexicon is crucial since we consider it as the _ground truth_ of the
emotional usage of words. For this reason, we employ ANEW bradley1999affective
, a well-known emotional lexicon developed and distributed by NIMH Center for
Emotion and Attention (CSEA) at the University of Florida (see Section 3). We
compute the Pearson correlation ($r$) between the standard and the CD EmoLex
as a measure of similarity. We repeat the whole procedure several times, and
then we calculate the average of the Pearson correlations ($\bar{r}$).
Finally, we compute the $SI$ as

$SI=\frac{1-\bar{r}}{2}$ (1)

An $SI$ value of $0$ corresponds to no diversity, i.e. emotional content
identical between the CD EmoLex and the standard lexicon. A value of $0.5$
corresponds to no correlation between the two lexicons, i.e. the emotional
content of words used by the community on Twitter is not related to the
standard emotional content. A value of $1$, which is very unlikely, would
correspond to the use of terms with the opposite emotional content compared to
standard.

The proposed $SI$ inevitably includes a component related to the performance
of the EmoSA algorithm. As explained in Section 4, EmoSA provides an estimate
based on a subset of tweets coming from a subset of the total community to be
analysed, and is based on several assumptions. Thus, this estimate unavoidably
includes some error. In fact, the correlation $r$, and in consequence the
value of $SI$, depends both on the different use of the language and on the
error of the algorithm. Even so, we believe that the $SI$ we propose can be
efficient in quantifying diversity. This because the error component should be
stable when changing the cultural mix of the community analysed, or at most it
can increase as multi-culturality increases, so it does not affect negatively
the relation between the $SI$ and diversity, but may even enhance it.

##  3 Lexical Resources and Data

To apply EmoSA (see section 4) and calculate $SI$, we take advantage of
several resources, which include, lists of lemmas tagged with sentiment
values, Twitter data and immigration data.

###  3.1 Lexical Resources

The reliability of the tagged lexicon is crucial for our algorithm. Among the
several prominent polarity lexicons attested in the literature, such as
Sentiment Orientation CALculator (SO-CAL) taboada2011lexicon , Sentiment
Treebank socher2013recursive , MPQA (Multi-Perspective Question Answering)
Subjectivity Lexicon (MPQA SL) riloff2003learning ; wilson2005recognizing ,
General Inquirer stone1966general , SentiWordNet esuli2007sentiwordnet , AFINN
nielsen2011new , and Opinion Lexicon hu2004mining ; liu2005opinion  we chose
ANEW bradley1999affective . The _Affective Norms for English Words_ (ANEW)
lexicon provides a set of normative emotional ratings for 1,034 words in terms
of _pleasure_ , _arousal_ , and _dominance_ 111Pleasure represents positive
versus negative emotions. Arousal finds the two extremes of the scale of
values in “calm” and “exciting”. The dominance dimension determines if the
subject feels in control of the situation or not. Each dimension ranges in
$[0,10]$..

ANEW, as well as AFINN, MPQA SL, and SO-CAL, and unlike, e.g., SentiWordnet,
is a manually tagged lexicon. This means that the polarity annotations tend to
be more accurate and reliable. For our algorithm, we used the _pleasure_
dimension as a sentiment valence, as evaluated by both male and female
subjects. Furthermore, the words in ANEW are tagged following a continuous
scale of values that allows us to capture different nuances between the
polarity values. Lexicons such as MPQA SL, Sentiment Treebank, and General
Inquirer includes not-continuos polarity scales. In the MPQA SL lemmas are
tagged as positive and negative with a further annotation for intensity
(strong, weak); Sentiment Treebank proposes a five-class classification (_very
positive_ , _positive_ , _neutral_ , _negative_ , and _very negative_);
General Inquirer and Opinion Lexicon classify lemmas as positive or negative.
Moreover, unlike the manually tagged lexicon SO-CAL in which sentiment label
is an integer in $[-5,+5]$, ANEW also includes objective words, and thus
neutral, words. Finally, as for instance SO-CAL and SentiWordNet, ANEW assigns
PoS tags to lemmas, unlike Opinion Lexicon and AFINN.

_SentiWordNet_ is a publicly available lexical resource. For each lemma in the
lexicon there are three numerical scores (_Obj(s)_ , _Pos(s)_ and _Neg(s)_)
describing how objective, positive, and negative the lemma is. In order to
compute a unique polarity score for each lemma, we adopt the difference
between the positive and negative score as an overall sentiment valence,
properly scaled to the interval [0, 10] guerini2013sentiment . Since
polarities’ distribution is strongly heterogeneous we balanced the obtained
lexicon by choosing the number (_n_) of items in the least represented class
(negative) and selecting from other classes the _n_ most strongly polarized
lemmas.

The _Full List of Bad Words Banned by Google_ of the “What Do You Love” (WDYL)
Google project is a list of 550 swear and curse terms222The service is now
inactive. The list can be downloaded from Free Web Header: Full List of Bad
Words Banned by Google. We manually enriched each word with a 0.0 valence
score, hence these terms are considered strongly negative.

Due to the multi-language nature of our experiment, we translate each of the
three English lexicons above into the Italian language. To obtain the most
accurate translation we combine and cross-check two different API services:
Googletrans333https://pypi.org/project/googletrans/ and
Goslate444https://pypi.org/project/goslate/. In addition, we also impose some
constraints, such a threshold on the translation confidence score. Finally, in
order to be more confident in the translation, we select only lemmas attested
in the PAISA’lyding2014paisa  corpus, which contains about two hundred and
fifty million tokens labelled with their frequency, retrieved from about three
hundred and eighty thousand Italian web texts.

###  3.2 Untagged Twitter Dataset

The untagged Twitter dataset is a subset of the one used in
coletto2017perception  and it is composed of just under 73,175,500
geolocalised tweets gathered for 3 months, from the 1st August to the 31st
October of 2015. From these, we have selected only the tweets originating in
the UK or Italy. They have been preprocessed to obtain cleaned tweets only
coming from the United Kingdom and Italy. The first problem faced is origin
and language selection, which was accomplished by using the metadata of the
tweets themselves. Specifically, for the language, we used the value provided
by the Twitter API, while for the geolocation we used the geographical
coordinates. Thus we selected only the tweets that contained the geographical
coordinates, and did not include those containing only the _place_ , since
this is not reliable information on the source of a tweet. To obtain texts
processable in an effective way by automatic methods, we employ both rule-
based and general-purpose NLP pipeline. Then, all tweets are lemmatised and
parts of speech are tagged using the POS tagger TreeTagger
schmid1994probabilistic ; schmid1995improvements . Finally, to reduce the
noise we selected only nouns, adjectives, and verbs. This selection allowed us
to obtain only significant words from the sentiment and meaning point of view.
At the end of the preprocessing phase, we obtained two cleaned and
standardised subsets: one composed of all the English tweets posted from the
United Kingdom, and one composed of all the Italian tweets posted from Italy.

A second preprocessing stage had as objective the assignment of tweets from
each dataset to a city in the UK or Italy. This was required to match
locations in D4I (see 3.4) to our results. Each tweet has associated a
location, including the city or town it came from. Thus, for each tweet, we
check if its origin is attested in D4I. If the city is found, we assign to the
tweet the related NUTS555The Classification of Territorial Units for
Statistics (NUTS), in French “ _Nomenclature des unités territoriales
statistiques_ ”, is a standard geocode referring the subdivisions of countries
for statistical purposes. The standard is developed and regulated by the
European Union.  code. Otherwise, we perform a dedicated rule-based pipeline.
This step is often required since in D4I locations are at the city level,
while several tweet origins are at the district or town level. First, the
MediaWiki API666MediaWiki API main page: MediaWiki is exploited by using the
tweet’s location as a key-word. When the API allows us to extract the city
referred to the tweet’s origin from the MediaWiki page’s info-box, we assign
it to the tweet. If not, the location value is used as a Google Search
API777Google Custom Search API page: Google Custom Search parameter in order
to extract URLs of the first five pages. From these, URLs pointing to
Wikipedia are selected and, as before, are used to extract the city referred
to by the location from the info-box. If the combination of the already
mentioned APIs does not allow us to retrieve a city attested in the D4I
dataset, we exploit the Google Maps API first and the Geopy888Geopy site:
Geopy python library then. These two are used after the first searching phase
due to their rate and call limits, even though they are generally more
accurate. All the retrieved cities are finally matched with those in the D4I
dataset and each tweet is labeled with NUTS codes at three levels (_NUTS1_ ,
_NUTS2_ , and _NUTS3_). Following this procedure, we are able to associate a
NUTS code with over 94% of the UK tweets and to over 97.5% of the Italian
tweets.

It is, however, worth pointing out that in both Twitter datasets there are
several tweets geo-labeled with the region/county or country name. Also, in
particular in the Italian dataset, many tweets are labeled with multi-language
city names translations, such as “Nápoles”, “Trentino-Alto Adigio”, “Venecia”,
“São Gimian” and “São Remo”, “Ancône”, “Naturns” and “Florencia”. Tweets
belonging the first case are discarded due to the geographical multilevel
nature of our analysis. In fact, country or region/county tags do not allow us
to reach a fine-grained geographical level. Instead, tweets belonging to the
second case are not ruled out a priori but they are rarely assigned a NUTS
code by our pipeline.

The size and number of cities covered by the two datasets after preprocessing
are showed in Table 1.

Dataset | # tweets | # matched cities
---|---|---
UK | 2,088,346 | 9,603
Italy | 274,885 | 6,050
Table 1: Dataset details for the UK and Italy.

###  3.3 Tagged Twitter Dataset

This dataset is composed of 3,718 publicly available tweets and their
sentiment classifications, retrieved from three different sources:

_The Semeval 2013 Message Polarity Classification competition (task B)_ 999The
Semeval 2013 Message Polarity Classification competition (task B),
https://www.cs.york.ac.uk/semeval-2013/. The original dataset consisted of a
12-20K messages corpus on a range of topics, classified into positive, neutral
and negative classes. We retrieved 2,752 such tweets, that were still
available on the Twitter platform. These were passed through the language
detection algorithm provided by the Python package Langdetect, to ensure they
were written in English, leaving us with 2,547 tweets in the final dataset
(428 negative, 1,347 neutral and 970 positive).

_The Semeval 2014 Message Polarity Classification competition (task B)_
101010The Semeval 2014 Message Polarity Classification competition (task B),
http://alt.qcri.org/semeval2014/. Similar to Semeval 2013, this corpus
consisted originally in 10000 tweets, out of which we downloaded 687 English
tweets (142 negative, 319 neutral and 226 positive).

_Earth Hour 2015 corpus_ 111111The Earth Hour 2015 corpus:
https://gate.ac.uk/projects/decarbonet/datasets.html. This dataset contains
600 tweets annotated with Sentiment information (Positive, Negative, Neutral)
where each annotation is triply-annotated through a crowdsourcing campaign.
Out of these, 370 tweets were still available for download through the Twitter
platform (30 negative, 185 neutral and 80 positive).

We observe that a small proportion of tweets is tagged with a negative
valence, with neutral tweets being most prevalent, in all three datasets. This
can be seen as a characteristic of Twitter messages in general. This dataset
will be used in the following to evaluate the performance of our community-
dependent emotional lexicon, compared to an already established lexicon, in
classifying tweet sentiment. The tweets are normalised and lemmatised similar
to the Untagged Twitter Dataset.

###  3.4 D4I Dataset

The D4I dataset d4i  contains the concentration of migrants in all cities of
eight EU countries: Spain, Germany, Italy, France, Netherlands, Portugal,
United Kingdom, and Ireland. Migrant counts are based on the 2011 EU
population census. Migrants are counted based on three different levels of
aggregation: by country, continent and EU versus non-EU. For our analysis, we
focused only on data for the UK and Italy and summing EU and non-EU immigrant
counts to obtain total immigration levels, since diversity is caused by all
migrant types.

##  4 Computing emotional valences from Twitter with EmoSA

Lexicon-based sentiment analysis methods use a lexicon of words tagged with an
emotional valence to assign sentiment to text, by aggregating the valences of
single words. These lexicons can be tagged manually, as in the case of ANEW
(Section 3), or can be generated automatically. While manual annotations can
be more accurate, lexicons can be too reduced to allow for accurate sentiment
analysis, especially for small texts such as tweets. Here we introduce EmoSA,
a method to extend the lexicon automatically starting from an existing seed
one, using sentiment spreading on a co-occurrence network of terms. The value
of our method is two-fold. First, it creates a much larger tagged lexicon
(namely Community-Dependent Emotional Lexicon (CD EmoLex)) that can be used
for lexicon-based sentiment analysis. Second, and most important for the
definition of our $SI$, the emotional valences attached to words depend on the
way words are used by the community, thus the resulting emotional valences can
be an indication of cultural diversity.

For a global vision, EmoSA is based on a starting text corpus, in our case an
untagged Twitter dataset (UTD, see Section 3.2), and on the standard emotional
lexicon ANEW (Section 3). The algorithm consists of two stages (see Algorithm
1). First we build a co-occurrence network of terms in the tweet dataset.
Subsequently, we initiate the epidemic spreading of emotional valences
starting from seed values. In the following we provide details for each phase.

To perform the EmoSA, we split the ANEW lexicon into two (50% each) equal
training ($TRAIN$) and test ($TEST$) datasets. The terms in the training
dataset are used for seeding EmoSA, while the test dataset is used to validate
the algorithm and to compute the $SI$.

Algorithm 1 EmoSA.

1:procedure compute_valences($TRAIN,TEST,UTD$)

2: $V,N\leftarrow build\\_network(UTD)$ $\triangleright$ build word network

3: $V^{*}\leftarrow sentiment\\_spreading(V,TRAIN,N)$

4: $V^{*}\leftarrow V^{*}\cap TEST$ $\triangleright$ Select valences of words
for the TEST dataset

5: return $V^{*}$

###  4.1 Building the network

Starting from an Untagged Twitter dataset (Section 3.2), we build a network of
lemmas where each lemma corresponds to one node. Two nodes are connected by an
edge if there is at least one tweet where both lemmas appear.

Hence, the network is an unweighted co-occurrence graph based on the target
tweets to be classified. We use a large amount of tweets to build this
network, thus we expect that lemmas with positive valence will be mostly
connected to other positive lemmas, while those with negative valences will be
connected among themselves. We consider only tweets not containing a negation
(“don’t”, “not”, etc.), since with negations it is difficult to understand
which lemmas from the negated tweet can be considered connected in the
network, and which not.

###  4.2 Sentiment Spreading

Once the network of lemmas is obtained, we start to add valences to each node
in the network. We start from a _seed lexicon_ , which is typically reduced in
size. In the next section we will show results obtained when the seed
($TRAIN$) is 50% of the ANEW lexicon (the other half is used to validate the
results, i.e. $TEST$ data), together with all lemmas in the SentiWordNet and
Bad words lexicon. This seed allows us to assign valences to a reduced number
of nodes in the network. This is the initial state of our epidemic process.

Starting from the initial state, we follow a discrete time process where at
each step sentiment valences spread through the network. At time $t$, for all
nodes $i$ which do not have any valence, the set of neighbouring nodes $N(i)$
is analysed, and $i$ takes a valence that aggregates the distribution of
valences in $N(i)$ if this distribution satisfies some basic properties, as
described bellow. The update is synchronous for all untagged nodes that can be
tagged at this time point. The epidemic procedure is repeated until no new
valences are assigned to the nodes, i.e. when the population reaches a stable
state. The process is similar to those seen in continuous opinion dynamics
models sirbu2017opinion , where agents take into account the aggregated
opinion of their entire neighbourhood when forming their own. The difference
here is that once a valence is assigned, it is never modified.

Let $V$ be the set of all nodes in the network $N$ and $S$ the set of initial
seed nodes with their valences $val_{s}(v_{i})$. The aim is to build a set
$V^{*}$ of nodes in $V$ with valences $val(v_{i})$ assigned. The procedure is
defined in Algorithm 2.

Algorithm 2 Sentiment Spreading

1:procedure sentiment_spreading($V,S,N$)

2: $V^{*}=\emptyset$

3: for $v_{i}\in S$ do$\triangleright$ Initialise valences with seed

4: $val(v_{i})\leftarrow val_{s}(v_{i})$

5: $V^{*}\leftarrow V^{*}\cup\\{v_{i}\\}$

6: repeat

7: $V^{*}_{old}\leftarrow V^{*}$

8: for $v_{i}\in V-v^{*}$ do

9: $v=\textbf{F}(N(v_{i}))$ $\triangleright$ $F(N(v_{i}))$ aggregates the
distribution of valences in $N(v_{i})$

10: if $v\neq NULL$ then

11: $val(v_{i})\leftarrow v$

12: $V^{*}\leftarrow V^{*}\cup\\{v_{i}\\}$

13: until $V^{*}=V^{*}_{old}$

14: return $V^{*}$

To decide the aggregation procedure, we took into account several
observations. In general, tweets appear to be very heterogeneous, most
containing both positive and negative words. Hence a simple averaging of
valences would most of the time result in neutral lemmas, although they
actually contain meaningful sentiment. So, we decided to use instead the
_mode_ of the distribution of valences in the neighbourhood, which is a much
more meaningful criterion in these conditions. However, the mode was only
considered in special circumstances, when the distribution of valences of
neighbours was not too heterogeneous. A heterogeneous distribution may have a
large range of valences is very large, or the entropy of the distribution can
be very high. In this case, it is unclear what the valence of the new lemma
should be, so we chose not to assign one at all. Again, this was inspired by
works from opinion dynamics (e.g. the q-voter model castellano2009nonlinear ),
taking into account the concept of social impact: agents are better able to
influence their neighbours as a consensual group rather than isolated, hence a
heterogeneous group will have no influence on its neighbours. Here, this was
implemented as thresholds on the range ($R$) and entropy ($S$) on the
neighbouring valence distribution: a node will be infected with the aggregated
valence of its neighbourhood only if the range and entropy are bellow these
thresholds. To avoid outliers, we consider the range to be the difference
between the 10th and the 90th percentile. The two thresholds $R$ and $S$
become two parameters of our model, that need to be tuned to maximise
performance.

Let $range^{*}$ and $entropy^{*}$ be the two thresholds. Then the procedure
$F$ taking as input $N(v_{i})$, the neighbours of $v_{i}$, can be described by
Algorithm 3.

Algorithm 3 Infection Function

1:procedure $F$($N(v_{i}),entropy^{*},range^{*}$)

2: $e=entropy(val(N(v_{i})))$ $\triangleright$ entropy of valences of nodes in
$N(v_{i})$

3: $r=range(val(N(v_{i})))$ $\triangleright$ range of valences of nodes in
$N(v_{i})$

4: if $e<entropy^{*}\And r<range^{*}$ then

5: return $mode(val(N(V_{i})))$

6: return NULL

With this definition of our spreading model, it is clear that the final
valences of the words are influenced by the initial seed lexicon, but also by
the structure of the network where emotional valences spread. This structure
is determined by the way Twitter users employ the language, hence it depends
on the cultural mix in the Twitter community. Thus, the final valences show
how the language is used in the corresponding community, from the point of
view of the emotional content of words.

##  5 Evaluation of emotional valences of words

To evaluate the emotional valences assigned by EmoSA to different words, we
apply the algorithm on tweets in English coming from the UK, from the Untagged
Twitter dataset (Section 3.2). The evaluation is based on two criteria. First,
we compare the emotional valences assigned by the algorithm with the standard
valences in ANEW, by computing the Pearson correlation on the test dataset.
Second, we use the community-dependent emotional lexicon for sentiment
classification of a Tagged Twitter dataset (Section 3.3) and we evaluate the
classification performance.

It is important to note that the evaluation of emotional valences of words
obtained by our algorithm is intrinsically difficult. One hand, we want the
valences on the _TEST_ dataset to be very close to those in ANEW. On the other
hand, however, we expect them to be different, and we actually use the
difference as a measure of cultural diversity, since we believe this can be
very useful in understanding effects of population migration both on the
receiving and incoming population. We expect that by changing the Twitter
population, the correlation between the two changes as well.

###  5.1 Evaluation

Figure 2 shows average correlation values after 10 runs with different
test/train splitting of the ANEW dataset, for different values of the
thresholds $S$ and $R$. We observe that correlations go up to 0.62, but only
when thresholds are very stringent, i.e. only very clear information is
allowed to spread in the network. When the distribution of valences in the
neighbourhood of a term is too heterogeneous, then it is better not to assign
any emotional value to that term. The largest correlation is obtained for a
range threshold of 3, and entropy threshold of 1.09 (we consider the
distribution described by 10 bins of equal size, hence the maximum entropy is
approximately 2.3), which are the parameters used in the rest of the paper for
UK data.

Figure 2: Average correlation between modelled and real word valences for the
UK data. Figure 3: Modelled and real word valence for one selected run with
best parameters.

Figure 3 displays the modelled and real valences on test data for the run with
the best correlation. The plot shows clearly that the valences obtained by our
method align well with human-tagged data, validating our approach. For further
comparison, we also display the distribution of valences in the ANEW lexicon,
compared to the community-dependent emotional lexicon obtained through our
method (Figure 4). We observe some differences here. While negative lemmas
make a small fraction of the lexicon in both cases, the CD EmoLex still
contains a larger fraction of neutral and positive lemmas, compared to the
ANEW lexicon. This is, however, not a concern, given that we observed this
trend for other lexicons as well (see description of SentiWordNet in Section
3).

Figure 4: Histogram of valences for ANEW and CD EmoLex.

A second criterion for validation of the CD EmoLex is classification
performance on the Tagged Twitter Dataset (Section 3.3). We implemented a
sentiment classifier based on Support Vector Machines (SVM), that used several
features to classify sentiment of tweets into three classes: negative, neutral
and positive. The features used include, for each tweet, several statistics
over the valence of individual lemmas contained by the tweet: arithmetic and
geometric mean, median, standard deviation, minimum and maximum. To these, we
added the number of lemmas with a valence over 7 and over 9, to understand the
presence of positive terms. Conversely, we also computed the number of lemmas
with a valence under 3 and under 1. Finally, we included the total length of
the tweet, and a boolean feature flagging the presence of a negation. We only
considered tweets for which at least 3 lemmas were found in the lexicon.

The features above can be computed using any lexicon, and SVM performance can
vary when changing the lexicon. We compare the performance of our CD EmoLex
(described in Figures 3 and 4) with that of ANEW. We expect no decrease in
performance with our community-dependent emotional lexicon. To validate
results, we used a cross-validation approach, where 80% of tagged tweets were
used to train the SVM and 20% to test it. The analysis was repeated ten times
for each lexicon, with average performance displayed in Figure 5. Error bars
show one standard deviation from the mean. The plot shows that the performance
with the CD EmoLex is comparable to ANEW, validating our approach. The F1
score increases on the negative class and decreases on the other two.
Precision increases on negative and neutral, while recall increases on
negative and positive tweets. Hence, our community-dependent emotional lexicon
seems to perform slightly better on negative tweets, however it overestimates
the positives. Given that negative tweets are a small part of the corpus,
accuracy decreases slightly. The performance range across repeated runs always
overlaps between the two lexicons compared.

Figure 5: Performance of SVM classifier using the original ANEW lexicon only
and our community-dependent emotional lexicon.

##  6 Superdiversity Index: computation and evaluation

Since we observed in the previous Section that the emotional valences assigned
to terms by our algorithm are in agreement with the standard valence, we now
proceed to check whether differences between communities can be related to
cultural diversity in a population. We compute the $SI$ at different space
resolutions for the UK and Italy, and consider as a baseline the foreign
immigration rates in the same geographical regions as those where the $SI$ was
computed. The immigration rates were extracted from the D4I dataset (Section
3.4). We expect that communities with higher immigration rates also have
higher diversity so higher $SI$. We calculate the Pearson correlation ($r$)
between the $SI$ values and immigration rates, as a measure of the performance
of the proposed $SI$.

###  6.1 Computing the SI

Algorithm 4 shows the steps needed to calculate the $SI$. After applying EmoSA
$compute\\_valences$, for each term $t$ in the $TEST$ dataset (with valence
$v_{t}$) we have a corresponding modelled valence $v^{*}_{t}\in TEST^{*}$,
which tells us how the word is actually used by the community. We then compute
the Pearson correlation, $r=pearson(TEST,TEST^{*})$, between the standard and
the community-dependent valences of words over all the words in the $TEST$
dataset.

Algorithm 4 SI computation

1:procedure superdiversity_index($ANEW,UTD,iteration\\_count$)

2: $\bar{r}\leftarrow 0$ $\triangleright$ Average Pearson correlation

3: for $i\in 0..iteration\\_count$ do$\triangleright$ Repeat many times

4: $TRAIN,TEST\leftarrow split(ANEW,0.5)$ $\triangleright$ Split ANEW 50%-50%

5: $TEST^{*}\leftarrow compute\\_valences(TRAIN,TEST,UTD)$ $\triangleright$
Compute valences from Twitter data

6: $r\leftarrow pearson(TEST^{*},TEST)$ $\triangleright$ Compute correlation
between modelled and real values

7: $\bar{r}\leftarrow\bar{r}+r$

8: $\bar{r}\leftarrow\bar{r}/iteration\\_count$

9: $SI\leftarrow(1-\bar{r})/2$ $\triangleright$ Compute $SI$ from correlation

10: return $SI$

In this work, we use ($iteration\\_count=10$). $SI$ takes values in the range
$[0,1]$.

###  6.2 SI evaluation

To prove that it is the community that determines the value of the SI, and
that our correlations are not random, we also devise a _null model SI_. This
is achieved by reshuffling tweets across geographical regions, maintaining
fixed the number of tweets in each region. We compute the correlation among
the null model $SI$ and immigration rates, and compare them with the original
$SI$ correlation.

An additional evaluation step compares the performance of the newly proposed
$SI$ with that of other possible superdiversity measures extracted from the
same data. We consider five additional measures. The first two relate to the
frequency of tweeting. One could hypothesise that a more diverse community
would tweet more, or less. Hence we consider the total number of tweets in the
local language, together with the population-normalised version, i.e. number
of tweets per capita. A second category of measures relates to the different
languages spoken by a Twitter community. We would expect that a more diverse
community will use more languages. We consider the absolute number of
languages, but also the entropy of the distribution of tweets in the various
languages. The latter measure takes into account the volume of tweets in each
language, besides the number of languages. The fifth possible measure of
diversity relates to the lexical richness of the language used by a twitter
community. Again, one could expect a richer language from a diverse community.
To quantify this, we use a well-known index used in Linguistics, the Token
Type Ratio (TTR) templin1957certain . It is computed as the ratio between the
number of token types (in our case the number of different words) that make up
the vocabulary and the overall size of the corpus (the total number of words
in the corpus). The TTR value ranges in [0,1], where values closer to 1 denote
that texts are varied and rich.

###  6.3 UK

The proposed $SI$ was computed for the UK at three different geographical
resolutions. The NUTS1 level corresponds to 12 UK regions while the NUTS2
level to 40 regions. The NUTS3 level contains 174 different regions, out of
which we select the 40 with the largest number of tweets. For computation of
the $SI$ we considered all the tweets in English published in the various
regions. Figure 6 shows visually the geographical distribution of $SI$ values
at level NUTS2, and compares with the distribution of foreign immigration
levels, from the D4I dataset. There is a clear similarity among the two maps.
To understand better the relation between $SI$ and immigration rates, Figure 7
plots the $SI$ values obtained versus the immigration rates, at each NUTS
level analysed. We observe that most of the regions align very well on a line,
with a very large correlation with the immigration rates.

At all geographical levels, we observe that the regions corresponding to
Northeast England and the London area appear to have a different behaviour,
deviating from the main line defined by the other regions. This is also
visible on the map, at regional level. However, when moving from NUTS1 to
NUTS2 and NUTS3, we see that, within the two regions, $SI$ grows as the
immigration rate grows. For instance, at level NUTS2, when considering only
the five regions from the London area, we see that $SI$ is larger when
immigration is larger. The same is true for the regions from Northeast
England. We believe this is due to different ranges of $SI$ in different
areas, and will discuss it further in Section 7. For the rest of this section,
we will consider only the remaining regions, i.e. all UK except for Northeast
England and the London area. Table 2 shows the exact values of the Pearson
correlation between immigration rates and the SI, which prove to be remarkably
well correlated at all geographical levels. The null model $SI$ does not
correlate at all, as expected, giving evidence that the correlations we
obtained are meaningful and related to the source community of the tweets, and
not merely due to the number of tweets in each region.

The comparison with other possible measures of diversity is also very
favourable to our proposed SI, which is clearly superior to all others, as the
same Table 2 shows. No relation between immigration rates and frequency of
tweets appears to exist. Some correlation seems to emerge with the number of
tweets per capita, number of languages and the language entropy, however much
lower than the $SI$ case, and not stable at all geographical levels.

Figure 6: Superdiversity index (left) and immigration levels (right) across UK
regions at NUTS2 level.

Figure 7: SI values versus immigration rates at different geographical levels, for the UK. At the level NUTS3 we selected the top 40 regions based on the number of tweets available in the dataset. The stars correspond to the London area, the triangles represent regions in Northeast England, while the rest of the regions are displayed with circles.  Geographical level |  $SI$ |  null model $SI$ |  number of English tweets |  number of English tweets per capita |  number of languages |  language entropy |  TTR
---|---|---|---|---|---|---|---
NUTS1 (10 regions) |  0.943 |  -0.236 |  0.328 |  -0.520 |  0.519 |  0.481 |  -0.005
NUTS2 (40 regions) |  0.941 |  -0.137 |  0.332 |  0.007 |  0.362 |  0.288 |  -0.340
NUTS3 (40 regions) |  0.928 |  -0.221 |  0.141 |  0.049 |  0.322 |  0.529 |  0.147
Table 2: Correlation between different measures of diversity extracted from
Twitter and the immigration rates, at various geographical levels in the UK,
excluding London and Northeast England. At the level NUTS3 we selected the top
40 regions based on the number of tweets available in the dataset.

###  6.4 Italy

The analysis above was repeated for Italy. We translated the tagged
dictionaries into Italian and applied the method to all Italian tweets
published from different Italian regions. We first report results for various
$S$ and $R$ parameters. Figure 8 shows that best results were obtained for
$R=3$ and $S=2.19$, which are the values used to obtain all the results
related to Italy presented in this section.

Figure 8: Optimisation of model parameters for Italy.

Figure 9: Superdiversity index (left) and immigration levels (right) across
Italian regions at NUTS2 level.

Figure 10: SI values versus immigration rates at different geographical levels, for Italy. At the level NUTS3 we selected the top 20 regions based on the number of tweets available in the dataset. Geographical level |  $SI$ |  null model $SI$ |  number of Italian tweets |  number of Italian tweets per capita |  number of languages |  language entropy |  TTR
---|---|---|---|---|---|---|---
NUTS1 (5 regions) |  0.963 |  -0.437 |  0.735 |  0.696 |  0.183 |  -0.585 |  -0.727
NUTS2 (20 regions) |  0.859 |  0.143 |  0.279 |  0.282 |  0.304 |  0.099 |  -0.243
NUTS3 (20 regions) |  0.924 |  0.082 |  0.081 |  -0.148 |  0.216 |  0.021 |  0.091
Table 3: Correlation between different measures of diversity extracted from
Twitter and the immigration rates, at various geographical levels in Italy. At
county level (NUTS3) we selected the top 20 regions based on the number of
tweets available in the dataset.

Figure 9 shows the geographical distribution of both the $SI$ values and the
immigration rates from the D4I dataset, at level NUTS2 (regional). Again,
there is a very good similarity among the two maps. In Figure 10 we plot the
$SI$ values obtained by our method versus the immigration rates. Excellent
correlation with immigration can be observed, at all geographical levels.
Exact correlations are reported in Table 3, with values over 0.85 at all
levels. Please note that at level NUTS1, Italy is divided into only 5 regions,
hence here correlations are not really meaningful. We report them for
completeness, however is is the behaviour at the following levels that carries
significance. At NUTS2 there are 20 regions, while at NUTS3 we select the top
20, similar to the UK case.

The table also shows results for the null model, which are as expected: the
null model $SI$ does not correlate with immigration rates. As for the other
possible diversity measures, none of them seem to give any hint of the
immigration rate, at levels NUTS2 and NUTS3. Hence our proposed $SI$ is
undoubtedly superior. At level NUTS1 we see some correlation, but again we
believe these values to be spurious, since we are considering only 5
geographical areas.

##  7 Discussion

The results presented above are very promising, and show a strong link between
the proposed $SI$ and foreign immigration rates. However, in the UK case, a
small number of regions seemed not to show the same behaviour as the rest of
the country. These were the London Area and Northeast England. In the first
case, although immigration rates in London are much higher than the rest of
the country, the $SI$ value extracted from all tweets in London was smaller
(NUTS1). However, when dividing tweets per county, within the London area,
$SI$ values seem to grow as immigration levels grow (NUTS2 and NUTS3). Hence,
it appears that the ranges of the $SI$ obtained are different in London
compared to the rest of the country. The same applies to Northeast England.
Even if immigration rates are low, $SI$ values were high. Again, those regions
seem to form a cluster of their own, where $SI$ ranges do not match those from
the rest of the UK. Thus, in order to make the $SI$ range uniform, we need to
identify correcting factors or at least determine the various clusters,
without using the immigration rate itself. This should also make $SI$ values
comparable across geographical resolutions, since we observe that, for the
same immigration rate, $SI$ values can vary from one NUTS level to another.

One possible correcting factor could be language entropy. We observed that the
London area displays, at NUTS1 level, a much higher language entropy compared
to the rest of the country, hence it can be used to rescale the SI. Figure 11
shows language entropy for all NUTS1 regions. In Italy, however, language
entropy is very similar across regions (figure 12), and in fact there seem not
to be any range issues here, since all regions overlap very well. The uniform
and large entropy in Italy could be explained by the fact that Italy is a
popular tourist destination, so many tweets in different languages exist. For
instance in central Italy we have 25044 tweets in English and 67903 in
Italian, a factor of only 2.7 between the local and a foreign language.

Figure 11: Language entropy on tweets originating from the UK, at macro scale
(NUTS1 regions). The region UKI corresponds to the London area, while UKC to
Northeast England. Figure 12: Language entropy on tweets originating from
Italy, at macro scale (NUTS1 regions).

A different important factor could be cultural differences of the local non-
immigrant community, such as those identifiable by local dialects. A higher
use of the local dialect could explain the apparent larger $SI$ values in
Northeast England. To correct for this, a baseline $SI$ value could be
computed from a subset of tweets coming from local users (for example local
newspapers or official accounts). This baseline could be used to correct for
range differences to the rest of the country.

Given the high correlation to foreign immigration rates, we believe that our
$SI$ is a first step towards a novel nowcasting model for migration stocks.
Once all correcting factors are identified, these could be used, together with
the SI, to build a highly accurate model to estimate immigration rates. We
expect that a standard machine learning model could prove suitable for this
task, that we plan to undertake in future work. Such a model will enable
accurate immigration statistics without the need for time- and resource-
consuming population censuses. By repeating the analysis regularly, we will be
able to maintain updated statistics, valid also in regions where censuses are
not possible or inaccurate due to clandestine immigration.

##  8 Related Work

The migratory phenomenon intrinsically concerns at least two countries;
therefore, its study implies the use of cross-referenced data coming from
among various nations. To date, most of the information about migration flows
and stocks derive from administrative data and official statistics, thus from
surveys and national censuses. Databases coming from different countries are
often inconsistent among themselves and show low spatial coverage and reduced
time resolution. The recent wide availability of Big Data allowed to employ
these new data in understanding and estimating the migration phenomenon and to
observe related consequences with high resolution and spatial coverage. Thanks
to their characteristics, it may be possible to extract new indexes for
estimating, nowcasting, and evaluating stocks and flows and integration of
immigrants. These data can highlight most of the important effects of
migration on both the migrant population and on the receiving and source
communities.

In Human Migration studies, works exploiting Big Data have been carried out
using, among others, mobile phone data, e-mail communication logs, Skype data,
and social media data. Notably, the latter contains various types of
information about users. As compared to traditional data sources, they are
potentially unlimited in terms of sample size since they cover a broad set of
the worldwide population. Online Social Networks (OSNs) can allow researchers
to analyze temporal variations easily, and to now-cast migration phenomena.

The literature on social media data for Human Migration studies includes
multiple social platforms. Rodriguez et al. rodriguez2014migration  and
Barslund et al. barslund2016mobile  used data gathered from LinkedIn to
identify mobility patterns of highly skilled migrants. Messias et al.
Messias2016  employed Google+ data to study location patterns of migrants who
have lived sequentially in more than two countries. Finally, in less recent
works, State et al. state2013studying  and Zagheni et al. zagheni2012you
exploited Yahoo!’s users’ data to infer global flows of both migrants and
tourists.

However, most studies focused on data from Twitter and, more recently,
Facebook. Herdagdelen et al. HerdagdelenAmac2016  inspected friendship ties of
immigrants in the United States (US) to study the composition of their social
networks on Facebook. Dubois et al. Dubois2018  and Zagheni et al.
zagheni2017leveraging  used data from Facebook’s advertising platform to
estimate assimilation of Arabic-speaking migrants in Germany, and stock of
international migrants in the US, respectively. Stewart et al. stewart2019rock
analyzed musical interests on Facebook to estimate the cultural assimilation
of Mexican immigrants in the US. Finally, Spyratos et al.
spyratos2018migration  computed independent estimates of Facebook Network
“expats” by correcting the selection bias of the platform users compared to
the real population.

Regarding Twitter, Steiger et al. provided a survey of using Twitter data for
research steiger2015advanced , reviewing 92 research papers. The study shows
that 57% of the papers take into account only semantic information (e.g.,
hashtags, followers, and profiles), that 33% rely on both semantic and spatio-
temporal information, and that the 10% exploits only spatio-temporal
information. Related to migration, Hawelka et al. hawelka2014geo  and Zagheni
et al. zagheni2014inferring  exploited geo-localized tweets to analyze trends
in both mobility and migration flows. Fiorio et al. fiorio2017using  estimated
US internal migration flows in different timing and durations and showed, for
the first time, relationships between short-term mobility and long-term
migration. Freire-Vidal et al. freire2019characterization  characterized the
behavior of locals of Chile towards immigrants and measured differences
between attitudes by using psycho-linguistic lexicons and interaction
networks.

Although the literature has highlighted potential ethics, selection bias, and
privacy issues, various research attested the importance of social media data
sources in understanding migration patterns. For instance, in a recent work,
Aswad et al. aswad2018refugee  investigate the role of social media to
understand whether it can be considered a proxy for immigration. The data
analysis confirms that Twitter may be regarded as a valid source for
information about immigration and refugee placement.

The data coming from OSNs can enable the study of the migratory phenomenon
also through the language used by users lamanna2018immigrant ;
hubl2017analyzing . Indeed, the language could represent a direct link between
individuals, origin country, and nationality. Moreover, the language allows
observing how linguistic characteristics of people vary when in contact with
other cultures. The analysis of the language used in social networks applied
to human migration studies involves several research fields, including but not
limited to mobility patterns mocanu2013twitter , migration stocks and flows
magdy2014exploiting ; lamanna2018immigrant , and sentiment analysis
senaratne2014moving .

Mocanu et al. mocanu2013twitter  proposed a global analysis of indicators and
linguistic trends starting from Twitter. By aggregating data based on
different geographical scales, the study characterized the worldwide
linguistic geography finding a universal pattern describing users’ activity
across countries. The authors studied various indicators, such as the
linguistic homogeneity of the different countries, the seasonal tourist
patterns within the countries, and the geographical distribution of the
different languages in the multilingual regions. The proposed analysis
suggests that there would be a high correlation between the heterogeneity of
Twitter penetration and the Gross Domestic Product (GDP). The results also
show that the statistical usage model of the social platform is independent of
factors such as country and language. Finally, by analyzing the temporal
variations of the linguistic composition by country, it is possible to observe
travel patterns and identify seasonal and real-time mobility patterns.

Magdy et al. magdy2014exploiting  proposed an analytical study of relations
between the language of tweets and their spatial distribution to investigate
a) language diversity and its usage in Twitter communities geolocalized by
country; and b) spatial distribution of cultural groups in different
countries. Starting from the identification of local communities, the authors
identified dominant languages and the spatial distribution of minor languages
in local communities. Linguistic diversity is calculated for each identified
local community through the use of statistical measures. Findings show that
measures taking into account the language distribution within the community
are more coherent and robust in identifying the countries with the highest
language diversity. Furthermore, by comparing linguistic diversity measures
with official data, the authors showed that Twitter’s community sample could
be representative of the actual population. Finally, based on the spatial
distribution within the countries, the study identified different cultural
groups. This task could be useful to understand certain behaviors related to
specific cultural groups such as Syrian refugees preferring to get closer to
communities with similar cultural traits.

Gonçalves et al. gonccalves2014crowdsourcing  exploited geolocalized tweets to
study diatopic variations of the modern-day Spanish language. The authors
applied machine learning techniques to inspect a large dataset of Spanish
vulgar tweets. Through these analyses, it is possible to identify, for the
first time, two varieties of the Spanish language. It seems that the Spanish
language attested on Twitter has two super dialects: an urban speech typically
used in the main Spanish and American cities, and a local speech mostly
attested in small towns and rural areas. The proposed approach also allowed
the authors to identify regional dialects and their approximate isoglosses.

Moise et al. moise2016tracking  explored the use of Big Data analytics for
tracking language mobility in geolocalised Twitter data. The authors proposed
a two-step quantitative analysis. First, the authors studied the temporal
evolution of languages on Twitter by investigating three case studies. Then,
the subject of the study shifts from the temporal to the spatial perspective
by observing the languages spread through monthly snapshots. Finally, starting
from the insights obtained through temporal and spatial analyses, they study
how linguistic mobility is reflected in the Twitter data. The insights
obtained by the authors show that the adoption of Twitter is heterogeneous
over time and reflects the linguistic distribution typical of the countries
with a multilingual panorama (e.g., Switzerland).

Finally, Lamanna et al. lamanna2018immigrant  proposed an extensive study on
the integration of immigrants using the Twitter language. First, the authors
characterized immigrants through digital space-time communication patterns and
then inferred immigrants’ places of residence and native languages. Then, a
modified entropy metric was introduced to quantify the so-called Power of
Integration of cities - i.e., its ability to integrate different cultures -
and to characterize the relationships between different cultures. While
underlining some of the well-known concerns related to the use of Twitter data
such as bias, the study seems to testify to the possibility of identifying
spatial patterns and mobility profiles using this type of data.

While previous works concentrate on one dimension of OSNs in general and
Twitter data in particular, our framework combines linguistic analysis,
sentiment analysis and spatio-temporal information from geolocalized tweets.
Moreover, our work includes two different languages, as opposed to previous
works which generally concentrate on one language only, typically English. An
important feature of our superdiversity index is the comparison with
immigration rates and the high correlations observed, comparison which in
previous works is not present or correlations are less relevant.

##  9 Conclusions

In this paper we proposed the novel Superdiversity Index ($SI$) that
quantifies diversity in a population based on the changes in emotional content
of words, compared to the standard language. We estimated this change in
communities from the UK and Italy, using geolocalised Twitter data in English
and Italian, at various geographical resolutions. In a majority of the
geographical regions analysed we observed a remarkable correlation with
foreign immigration rates, extracted from the dataset available from the Joint
Research Center of the European Community, through the D4I data challenge. The
proposed Superdiversity Index greatly outperforms other possible measures of
diversity form the same Twitter data.

The method has been tested here for two countries and languages, following the
nationality of the authors, but we plan to extend the analysis to other
European countries. This work paves the way for a novel nowcasting model of
immigration rates, that can be applied with higher time and space resolution
compared to official statistics. In future work we plan to investigate the use
of machine learning to achieve this task. We expect our superdiversity index
to be a major feature in this model, with various other features used to
correct for range differences, including language entropy, local dialects and
population density.

## References

  * (1) Aswad, F.M.S., Menezes, R.: Refugee and immigration: Twitter as a proxy for reality.  In: The Thirty-First International Flairs Conference (2018)
  * (2) Barslund, M., Busse, M.: How mobile is tech talent? a case study of it professionals based on data from linkedin.  A Case Study of It Professionals Based on Data from LinkedIn (June 30, 2016). CEPS Special Report (140) (2016)
  * (3) Bradley, M.M., Lang, P.J.: Affective norms for english words (anew): Instruction manual and affective ratings.  Tech. rep., Citeseer (1999)
  * (4) Castellano, C., Muñoz, M.A., Pastor-Satorras, R.: Nonlinear q-voter model.  Physical Review E 80(4), 041129 (2009)
  * (5) Centre, E.J.R.: Data challenge on integration of migrants in cities (2018).  URL https://bluehub.jrc.ec.europa.eu/datachallenge/
  * (6) Coletto, M., Esuli, A., Lucchese, C., Muntean, C.I., Nardini, F.M., Perego, R., Renso, C.: Perception of social phenomena through the multidimensional analysis of online social networks.  Online Social Networks and Media 1, 14–32 (2017)
  * (7) Dubois, A., Zagheni, E., Garimella, K., Weber, I.: Studying Migrant Assimilation Through Facebook Interests (2018)
  * (8) Esuli, A., Sebastiani, F.: Sentiwordnet: a high-coverage lexical resource for opinion mining.  Evaluation pp. 1–26 (2007)
  * (9) Fiorio, L., Abel, G., Cai, J., Zagheni, E., Weber, I., Vinué, G.: Using twitter data to estimate the relationship between short-term mobility and long-term migration.  In: Proceedings of the 2017 ACM on Web Science Conference, pp. 103–110. ACM (2017)
  * (10) Freire-Vidal, Y., Graells-Garrido, E.: Characterization of local attitudes toward immigration using social media.  In: Companion Proceedings of The 2019 World Wide Web Conference, pp. 783–790. ACM (2019)
  * (11) Gonçalves, B., Sánchez, D.: Crowdsourcing dialect characterization through twitter.  PloS one 9(11), e112074 (2014)
  * (12) Guerini, M., Gatti, L., Turchi, M.: Sentiment analysis: How to derive prior polarities from sentiwordnet.  arXiv preprint arXiv:1309.5843 (2013)
  * (13) Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., Ratti, C.: Geo-located twitter as proxy for global mobility patterns.  Cartography and Geographic Information Science 41(3), 260–271 (2014)
  * (14) Herdagdelen, Amaç, State, B., Adamic, L., Mason, W.: The social ties of immigrant communities in the United States.  In: WebSci (2016)
  * (15) Hu, M., Liu, B.: Mining and summarizing customer reviews.  In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 168–177 (2004)
  * (16) Hübl, F., Cvetojevic, S., Hochmair, H., Paulus, G.: Analyzing refugee migration patterns using geo-tagged tweets.  ISPRS International Journal of Geo-Information 6(10), 302 (2017)
  * (17) Kim, J., Sîrbu, A., Giannotti, F., Gabrielli, L.: Digital footprints of international migration on twitter.  In: International Symposium on Intelligent Data Analysis, pp. 274–286. Springer (2020)
  * (18) Lamanna, F., Lenormand, M., Salas-Olmedo, M.H., Romanillos, G., Gonçalves, B., Ramasco, J.J.: Immigrant community integration in world cities.  PloS one 13(3), e0191612 (2018)
  * (19) Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web.  In: Proceedings of the 14th international conference on World Wide Web, pp. 342–351 (2005)
  * (20) Lyding, V., Stemle, E., Borghetti, C., Brunello, M., Castagnoli, S., Dell’Orletta, F., Dittmann, H., Lenci, A., Pirrelli, V.: The paisa’corpus of italian web texts.  In: 9th Web as Corpus Workshop (WaC-9)@ EACL 2014, pp. 36–43. EACL (European chapter of the Association for Computational Linguistics) (2014)
  * (21) Magdy, A., Ghanem, T.M., Musleh, M., Mokbel, M.F.: Exploiting geo-tagged tweets to understand localized language diversity.  In: Proceedings of Workshop on Managing and Mining Enriched Geo-Spatial Data, p. 2. ACM (2014)
  * (22) Messias, J., Benevenuto, F., Weber, I., Zagheni, E.: From migration corridors to clusters: The value of Google+ data for migration studies.  In: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 421–428. IEEE (2016)
  * (23) Mocanu, D., Baronchelli, A., Perra, N., Gonçalves, B., Zhang, Q., Vespignani, A.: The twitter of babel: Mapping world languages through microblogging platforms.  PloS one 8(4), e61981 (2013)
  * (24) Moise, I., Gaere, E., Merz, R., Koch, S., Pournaras, E.: Tracking language mobility in the twitter landscape.  In: Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on, pp. 663–670. IEEE (2016)
  * (25) Nielsen, F.Å.: A new anew: Evaluation of a word list for sentiment analysis in microblogs.  arXiv preprint arXiv:1103.2903 (2011)
  * (26) Pollacci, L., Sîrbu, A., Giannotti, F., Pedreschi, D., Lucchese, C., Muntean, C.I.: Sentiment spreading: An epidemic model for lexicon-based sentiment analysis on twitter.  In: Conference of the Italian Association for Artificial Intelligence, pp. 114–127. Springer (2017)
  * (27) Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions.  In: Proceedings of the 2003 conference on Empirical methods in natural language processing, pp. 105–112 (2003)
  * (28) Rodriguez, M., Helbing, D., Zagheni, E., et al.: Migration of professionals to the us.  In: International Conference on Social Informatics, pp. 531–543. Springer (2014)
  * (29) Schmid, H.: Probabilistic part-of-speech tagging using decision trees. in proceedings of international conference on new methods in language processing,(pp. 1-9), access date: 09.11. 2012 (1994)
  * (30) Schmid, H.: Improvements in part-of-speech tagging with an application to german.  In: In proceedings of the acl sigdat-workshop. Citeseer (1995)
  * (31) Senaratne, H., Bröring, A., Schreck, T., Lehle, D.: Moving on twitter: using episodic hotspot and drift analysis to detect and characterise spatial trajectories.  In: Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, pp. 23–30 (2014)
  * (32) Sîrbu, A., Andrienko, G., Andrienko, N., Boldrini, C., Conti, M., Giannotti, F., Guidotti, R., Bertoli, S., Kim, J., Muntean, C.I., et al.: Human migration: the big data perspective.  International Journal of Data Science and Analytics pp. 1–20 (2020)
  * (33) Sîrbu, A., Loreto, V., Servedio, V.D., Tria, F.: Opinion dynamics: models, extensions and external effects.  In: Participatory sensing, opinions and collective awareness, pp. 363–401. Springer (2017)
  * (34) Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank.  In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp. 1631–1642 (2013)
  * (35) Spyratos, S., Vespe, M., Natale, F., Weber, I., Zagheni, E., Rango, M.: Migration data using social media: a european perspective (2018)
  * (36) State, B., Weber, I., Zagheni, E.: Studying inter-national mobility through ip geolocation.  In: Proceedings of the sixth ACM international conference on Web search and data mining, pp. 265–274 (2013)
  * (37) Steiger, E., De Albuquerque, J.P., Zipf, A.: An advanced systematic literature review on spatiotemporal analyses of t witter data.  Transactions in GIS 19(6), 809–834 (2015)
  * (38) Stewart, I., Flores, R.D., Riffe, T., Weber, I., Zagheni, E.: Rock, rap, or reggaeton?: Assessing mexican immigrants’ cultural assimilation using facebook data.  In: The World Wide Web Conference, pp. 3258–3264. ACM (2019)
  * (39) Stone, P.J., Dunphy, D.C., Smith, M.S.: The general inquirer: A computer approach to content analysis. (1966)
  * (40) Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis.  Computational linguistics 37(2), 267–307 (2011)
  * (41) Templin, M.C.: Certain language skills in children; their development and interrelationships. (1957)
  * (42) Vertovec, S.: Super-diversity and its implications.  Ethnic and racial studies 30(6), 1024–1054 (2007)
  * (43) Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis.  In: Proceedings of human language technology conference and conference on empirical methods in natural language processing, pp. 347–354 (2005)
  * (44) Zagheni, E., Garimella, V.R.K., Weber, I., et al.: Inferring international and internal migration patterns from twitter data.  In: Proceedings of the 23rd International Conference on World Wide Web, pp. 439–444. ACM (2014)
  * (45) Zagheni, E., Weber, I.: You are where you e-mail: using e-mail data to estimate international migration rates.  In: Proceedings of the 4th Annual ACM Web Science Conference, pp. 348–351. ACM (2012)
  * (46) Zagheni, E., Weber, I., Gummadi, K., et al.: Leveraging facebook’s advertising platform to monitor stocks of migrants.  Population and Development Review 43(4), 721–734 (2017)