Introduction

The following data comes from termometropolitico.it and refer to the period of time 18 December 2017 to 24 December 2017. Only parties with 5%+ have been included.

Party	Initials	%	Leader	Orientation
Movimento 5 Stelle	M5S	27.7	Luigi di Maio†	Anti-establishment
Partito Democratico	PD	24.2	Matteo Renzi	Centre-left
Forza Italia	FI	15.7	Silvio Berlusconi††	Centre-right
Lega Nord	Lega	13.7	Matteo Salvini	Far-right
Libertà e Uguaglianza	LeU	6.9	Pietro Grasso	Far-left
Fratelli d’Italia	FDI	5.2	Giorgia Meloni	Far-right

†Many would argue that the de facto leader of the party is Beppe Grillo; as Luigi di Maio won the most recent primary elections, I decided to go with him.

††In the rest of the post I will refer as these politicians simply as candidates, even though Berlusconi may or may not be the actual prime minister candidate of his party.

The Analysis

Data Collection

The data have been collected using Twitter API (via tweepy). Specifically:

• Used Twitter API to get all the tweets posted by candidates from January 1, 2017 to December 24, 2017 (extremes included). Retweets were ignored
• The code for data collection is available here
• For the duration of the analysis the tweets were stored in a local database to avoid re-querying the Twitter API multiple times (both raw and preprocessed data are available on GitHub)

Descriptive Analysis

For each candidate, a number of descriptive statistics were computed. The full code can be found here.

	GiorgiaMeloni	PietroGrasso	berlusconi	luigidimaio	matteorenzi	matteosalvinimi
num_tweets	617	143	651	420	518	3188
average_length	149.84	147.16	171.84	128.77	126.75	127.00
average_hashes	1.84	1.03	1.03	0.63	1.23	1.28
average_mentions	0.33	0.54	0.23	0.36	0.14	0.11
average_links	0.99	0.92	0.33	0.92	0.58	0.74

Note: because of the way Twitter works, average_links refers to both external links and images.

Sentiment Analysis

I used the Polyglot package to compute some rough sentiment scores for each candidate’s tweets. I chose Polyglot as it’s one of the few packages to offer localization in Italian. Note that Polyglot only offers a polarity score (-1.0, 0.0 or +1.0) for words. Sentiment scores were then computed by averaging the polarity scores for each token.

Specifically:

• Loaded the preprocessed data from TinyDB
• Computed polarity for each token via Polyglot
• For each tweet an average polarity was computed (ignoring tokens with polarity equal to zero)
• Tweets with an average polarity (i.e. sentiment score) smaller than zero were labeled as negative, with an average polarity equal to zero neutral and with an average polarity higher than zero positive
• The code is available here

The results are summarized below. Candidates are presented from far-left to far-right, with anti-establishment party M5S in the middle.

Right parties seem to have an higher percentage of negative tweets. While this could be the result of a precise communication strategy, it’s also important to note that the government was left-wing in 2017 and thus it makes sense for right parties to be more critical about the overall economical and political state of the country.

Keywords Analysis

For each candidate, a list of 25 keywords was computed by analysing their tweets and comparing them against the other five candidates.

Specifically:

• Loaded the preoprocessed data from TinyDB
• Created a single string containing for each candidate all of her or his tweets
• Computed the tfidf matrix
• Selected the top 25 words with highest tfidf score for each candidate

The results are summarized below. Candidates are presented from far-left to far-right, with anti-establishment party M5S in the middle.

PietroGrasso	matteorenzi	luigidimaio	berlusconi	GiorgiaMeloni	matteosalvinimi
storiedisangueamiciefantasmi	avanti	renzi	lintervista	italia	salvini
grazie	lingotto	governo	elezionisicilia	governo	lega
vittime	trenopd	oggi	tgcom24	oggi	italia
senato	lavoro	diretta	italia	amministrative2017	stopinvasione
anni	oggi	sceglieteilfuturo	musumecipresidente	sindaco	italiani
oggi	italia	italia	italiani	intervista	primagliitaliani
palermo	assembleapd	stelle	stato	italiasovrana	andiamoagovernare
mafia	insieme	rally	portaaporta	renzi	dimartedi
ricordo	scuolapd	movimento	settegiorni	italiani	lintervista
presto	futuro	sindaci5stelle	paese	roma	live
esempio	millegiorni	ospite	confapi	immigrati	ottoemezzo
libera	portaaporta	voto	governo	piazza	amici
ucciso	europa	sicilia	europa	tempodipatrioti	governo
libreria	grazie	tour	politica	appelloaipatrioti	portaaporta
impegno	istat	grazie	matrix	diretta	congressolega
italia	perché	solo	programma	anni	anni
legge	finestra	paese	anni	aspetto	renzi
stato	scienza	prima	tasse	fratelli	gabbiaopen
9maggio	politica	grande	chetempochefa	europa	casa
liberieuguali	democratica	italiani	molto	immigrazione	video
piolatorre	andiamo	legge	solo	sostenere	agorarai
bellissimo	cosa	sera	fatto	seguitemi	immigrati
voce	prima	lotti	lavoro	atreju17	diretta
auguri	tempo	renziconfessa	fiscale	candidatura	matrix
insieme	leopolda	rispettoperzuccaro	oggi	nazionale	facciamosquadra

Few observations in random order:

• Many keywords in Grasso’s vocabulary refer to Mafia (e.g., victims, 9may, Palermo, mafia, killed, etc.), which makes sense given that Grasso has been for many years Prosecutor at the Court of Palermo;
• Luigi Di Maio, Giorgia Meloni and Matteo Salvini all have renzi in their vocabulary (referring to Matteo Renzi, leader of PD). Interestingly, Berlusconi doesn’t;
• Unsurprisingly, the two far-right parties all have many immigration-related keywords (e.g., immigrants, immigration, stoptheinvasion, italiansfirst, etc.);
• Salvini is the only candidate to have his own name as a keyword. This may be simply the result of him having named his “sub-party” Noi con Salvini (en: Us With Salvini).

It’s also interesting to note how candidates do not seem to be communicating about the same issues from different points of view (e.g., immigration is good vs. immigration is bad) as much as talking about completely different topics. In other words, nobody seems to be offering an opposite narrative to other candidates’.