=Paper=
{{Paper
|id=Vol-3340/39
|storemode=property
|title=Data Analytics on Twitter for Evaluating Women Inclusion and Safety in Modern Society
|pdfUrl=https://ceur-ws.org/Vol-3340/paper39.pdf
|volume=Vol-3340
|authors=Loredana Caruccio,Stefano Cirillo,Vincenzo Deufemia,Giuseppe Polese,Roberto Stanzione,Giuseppina Andresini,Andrea Iovine,Roberto Gasbarro,Marco Lomolino,Marco de Gemmis,Annalisa Appice,Bruno Casella,Roberto Esposito,Carlo Cavazzoni,Marco Aldinucci
|dblpUrl=https://dblp.org/rec/conf/itadata/CaruccioCDPS22
}}
==Data Analytics on Twitter for Evaluating Women Inclusion and Safety in Modern Society==
https://ceur-ws.org/Vol-3340/paper39.pdf
Data Analytics on Twitter for Evaluating Women
Inclusion and Safety in Modern Society
Loredana Caruccio, Stefano Cirillo, Vincenzo Deufemia, Giuseppe Polese and
Roberto Stanzione
University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Salerno, Italy
Abstract
The inclusion and safety of women in modern society is a problem that over the years has become
increasingly important in all countries, leading to a large number of awareness campaigns and social
movements. Thus, several research communities have investigated various aspects of this problem,
mainly focusing on interpreting the behaviors of people, also according to their geopolitical situations, for
preventing and reducing discrimination situations against women. In this paper, we present a study on
the resonance of the gender equality issues on the Twitter social network. In particular, we collected 4.4
million tweets shared by over 1 million users and applied data analytics techniques to evaluate statistics,
correlations between the most common hashtags, and location-based sentiment analysis.
Keywords
Big Data Analytics, Gender Equality, Woman Inclusion, Twitter, Sentiment Analysis
“I raise up my voice – not so that I can shout, but so that those without a voice can be heard”.
Malala Yousafzai
1. Introduction
The inclusion of women in society represented one of the main challenge that accompanied
the history in the last centuries. In fact, although women always contributed to the cultural,
scientific, and economic growth of the society, they had to fight for obtaining own rights and
for starting in being considered equal to the men. The major examples of women’s battles are
related to suffrage campaigns, which gave women the right of voting only in the last century
(in most cases). Nevertheless, although from a legal point of view all women have the right to
vote, in some regions appear quite difficult for them to apply their right due to societal norms,
harassment, and violence at the polls, or pressure from their husbands1 .
The gender equality still represents an important achievement to be addressed in the modern
society. In fact, it has been included into the 17 Sustainable Development Goals defined by the
United Nations (UN) in 20152 . Within it, the women’s empowerment and gender equality have
been structured as 9 specific worldwide targets that must be reached until 2030, which range
ITADATA2022: The 1𝑠𝑡 Italian Conference on Big Data and Data Science, September 20–21, 2022, Milan, Italy
Envelope-Open lcaruccio@unisa.it (L. Caruccio); scirillo@unisa.it (S. Cirillo); deufemia@unisa.it (V. Deufemia);
gpolese@unisa.it (G. Polese); r.stanzione9@studenti.unisa.it (R. Stanzione)
Orcid 0000-0002-2418-1606 (L. Caruccio); 0000-0003-0201-2753 (S. Cirillo); 0000-0002-6711-3590 (V. Deufemia);
0000-0002-8496-2658 (G. Polese)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
CEUR Workshop Proceedings (CEUR-WS.org)
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
1
https://worldpopulationreview.com/country-rankings/countries-where-women-cant-vote
2
https://www.globalgoals.org/goals/5-gender-equality/
from the ending of discrimination and violence against women to the inclusion of women in
political, economical, and decision-making processes (by also promoting it through technology).
In fact, different studies highlighted that the gender equality not only represents a human
right issue, but also a precondition for the sustainable development [1], even though gender
inequalities across economic, social and environmental dimensions remain widespread and
persistent, especially in some areas of the world [2]. Consequently, many social movements and
organizations were born with the mission of promoting the gender equality and highlighting
any discrimination towards the woman empowering in the society. They typically feed the
worldwide resonance of the problem, enabling people to be informed, think, and act against it.
In this paper, we present the analysis that we performed on the resonance of the gender
equality issues over the Twitter social network. In particular, we collected 4,4 million tweets
shared by over 1 million users across the world, in a topic-specific dataset, named wishes-
Women Inclusion and Safety for Holding Equality in Society. Furthermore, after an hydrating
process, the dataset has been first used to perform a quantitative analysis highlighting major
statistics of collected tweets, which have been successively used to perform a sentiment analysis
by also considering the geopolitical scenario of different countries.
The remainder of the paper is organized as follows. In Section 2 we describe relevant works
concerning previous analysis over the gender equality issues. Then, Section 3 introduces the
wishes dataset, by highlighting the building process we considered, and the main statistics
concerning collected tweets. Instead, the evaluation of the people perception, performed through
a sentiment analysis, is presented in Section 4. Then, final considerations and future directions
conclude the paper.
2. Related Work
Over the past decade, social media platforms have been an important means of communication
for people, and their impact on daily lives is increasing every day. Thanks to their accessibility,
social networks represent a big source of information that can be used to extract insights for
analyzing collective thinking about specific issues, such as the role of women in the modern
society. In this section, we provide an overview of some related works dealing with the issues
of violence against women and the digital gender divide, which represent the main topics we
considered throughout the proposed analysis.
In [3] the authors analyzed 2,5 million tweets and then compared tweets about VAW (Violence
Against Women) with tweets on other topics like politics or sports. In particular, they discovered
that for the VAW topic there is a higher average of users and tweets per thread and also a higher
average of thread depth. These tweets have been further analyzed in [4], in which authors
focused their study on tweets in Arabic, by performing a sentiment analysis to extract insights
on how the issue of VAW is treated, and they noticed that this topic is gaining attention in the
Arabic world. Similar analysis also focused to South African [5] and Indonesian tweets [6].
In other recent studies, authors have extracted and analyzed the most used terms and the most
discussed topics related to domestic violence in the shared tweets [7] and in news articles [8],
by mainly focusing the analysis on articles related to gender-based violence from newspapers
in India, Pakistan, and the United Kingdom. In [9], the authors conducted a similar analysis
in Australia, considering live chats on Twitter related to a special Q&A episode dedicated to
the topic of domestic violence. The authors collected tweets written from the beginning of
the episode until the next day, and the results of their study showed that there were strongly
conflicting opinions and no agreement on condemning domestic violence. Other Recent articles,
such as [10] and [11], investigated the possible correlation between misogynistic tweets and
episodes of violence in the United States. In particular, the first study reported a correlation
with the number of rapes per capita, while the second one highlighted a correlation between
tweets and episodes of domestic violence.
Another topic on which the research has focused is the digital gender divide (DGD), which
indicates imbalances in access to Information and Communication Technologies (ICTs). Several
studies focus on developing countries [12, 13, 14], highlighting the obstacles faced by women
in the use of technology. In these studies, authors mention lack of education, unfavorable
employment, and income conditions as main causes of DGD.
As widely discussed in [15] and [16], the DGD problem does not exist only in developing
countries. In fact, these studies have highlighted how this disparity is also present in Europe.
Among the mentioned factors, there are stereotypes, prejudice, male hostility, and other women-
related problems like pregnancy and maternity. Quantifying DGD is not always easy because of
the lack of data, especially in low-income countries. To address this problem, in [17] the authors
proposed a method to track the gender digital gap using Facebook’s advertisement audience
estimates. They showed that with the proposed method it is possible to extend the calculation
of the gender gap indicators to more countries, especially low and middle-income ones.
Unlike the papers introduced above, the proposed study introduces a new up-to-date dataset
of tweets related to the role of women in different geopolitical areas, and provides a detailed
multilingual sentiment analysis to evaluate the inclusion and safety of women in modern society.
3. The wishes dataset
In this section, we provide an overview of the wishes dataset and analyze the different types of
tweets collected from Twitter. Moreover, we discuss the strategy for retrieving the content of
tweets with the aim to extract statistics from them and analyze the correlation between the most
frequent hashtags. Finally, we provide a preliminary analysis of people’s collective thoughts by
analyzing the content of the tweets. wishes dataset is publicly accessible on GitHub3 .
3.1. Overview of the wishes Dataset
The creation of the dataset has required the definition of an extraction module capable of
continuous monitoring tweets shared by users and collecting the most pertinent ones. This
module has been configured for interacting with Twitter streaming API and reading the tweets
related to a set of keywords concerning the role of women in the society and domestic violence.
To define the set of keywords involved in our monitoring, we started from a small set of
popular keywords manually extracted from the most popular hashtags used on Twitter that have
been provided by online services4 , and collected tweets for one day aiming to extract keywords
that co-occur with the initial ones. We have considered a set over 30 keywords representing
3
https://github.com/Roby46/Wishes_tweets_dataset
4
Tweeplers
107
Number of tweets 106
105
104
103
102
101
100
0
Turkish
English
Spanish
Greek
French
Tagalog
Japanese
Hindi
Dutch
Indonesian
Arabic
Portuguese
Korean
Tamil
Italian
Danish
Gujarati
Basque
Norwegian
Lithuanian
Telugu
Bengali
Malayalam
Amharic
Nepali
Icelandic
Divehi
Vietnamese
Hebrew
Panjabi
Slovenian
Latvian
Ukrainian
Sinhala
Oriya
Serbian
Bulgarian
German
Urdu
Estonian
Catalan
Thai
Haitian
Polish
Persian
Chinese
Romanian
Finnish
Swedish
Marathi
Russian
Welsh
Czech
Hungarian
Kannada
C. Kurdish
Sindhi
Lao
Pushto
Burmese
Tibetan
Armenian
C. Khmer
Georgian
Figure 2: Language distribution of tweets in wishes dataset.
the most used in tweets from around the world, such as “#abuseisnotlove”, “#youarenotalone”,
“#domesticabuse”, or “#domesticviolence”.
The collection step has been continuously performed since November 10th, 2021, and by
February 25th, 2022, achieving about 4.4 million tweets shared by over 1 million users. According
to Twitter policies, during the monitoring steps, we stored only the IDs of the tweets, requiring
the consequent hydration of them for analyzing their contents. In particular, the hydration
process of tweets has requested to connect with the Twitter API for accessing to the content of
the tweets. Table 1 shows statistics on tweets extracted before and after the hydration process.
As we can see, the number of tweets has been reduced to 3,760,574, from which 17,537 are
written by verified users, whereas 1,170,868 represent unverified tweets. This could be due to
the fact that either the tweets were written by users who have been banned on Twitter, or they
represent tweets that have been removed from the platforms.
1e6
Before After 3.5
Number of tweets
Hydrating Hydrating 3.0
Number of tweets 4,489,070 3,760,574 2.5
Number of accounts - 1,170,868 2.0
Verified accounts - 17,537 1.5
1.0
Accounts with location - 2,507 0.5
Average tweets per account - 3.21 0.0 All Tweets Retweets Tweet with Plain text
Most recent tweet 2022-02-25 mentions tweets
Table 1: Details of wishes dataset. Figure 1: Types of tweet in wishes dataset.
After hydrating the content of the tweets, we performed a cleaning process by removing
all special characters and/or emojis, yielding the standardization of the tweet syntax. This
operation allowed us to identify the most common hashtags used in tweets and analyze their
frequency in the dataset.
Figure 1 shows the different types of tweets shared by both verified and non-verified accounts
[18]. As we can see, among the different types of tweets, there are about 2.5 millions of retweets
(66% of all tweets), about 500,000 of tweets contain at least one mention towards other users
(13% of all tweets), and about 1 million of tweets are plain text tweet (26% of all tweets), i.e.,
tweets written in a standard text-based format that are not retweets and do not contain mentions.
Figure 2 shows the number of tweets for each language collected in wishes. As we can see,
wishes contains tweets written in 64 different languages, most of which are written in English,
Spanish, and Turkish.
In order to perform a more detailed analysis of the collective thinking on the role of women
in society and on the problem of domestic violence, we provide a preliminary analysis of the
most frequent hashtags used in the tweets as shown in Figure 3. As we can see, among the
most frequent hashtags (i.e., the hashtags with the largest font sizes), there are hashtags that
seem to not be related to the purpose of the proposed study, such as “#quoteofthedays” and
“#endsars”. In fact, although the former is a generic hashtag used by Twitter users when writing
a sentence for the day, the latter refers to a decentralized social movement and series of mass
protests against police brutality in Nigeria. Nevertheless, in both cases, the tweets including
these hashtags also contain references to the woman and their roles in society. Furthermore, it
is possible to notice that most hashtags have targeted references closer to the scope of wishes,
such as “#metoo”, “#domesticviolence”, “#selflove”, “#heforshe”, and “#neverforget”.
Table 2 shows the occurrences and the frequencies of the top 30 hashtags used in the collection
of tweets. The frequency is the number of occurrences of each hashtag with respect to the
tweets shared from both verified and non-verified accounts. It is important to notice that only
2,741,802 of 3,760,574 tweets contain at least one hashtag, which corresponds to 73% of all the
tweets. As shown in Table 2, we provide three different values representing the frequency of
each hashtag with respect to 𝑖) the number of all the hashtags used in the tweets (i.e., 𝑁𝑋 ); 𝑖𝑖)
the number of tweets that contain at least one hashtag (i.e., 𝑁𝑌 ), and 𝑖𝑖𝑖) the number of all tweets
in the data collection (i.e., 𝑁𝑍 ). In particular, let 𝑁 be the number of occurrences of each hashtag
in the data collection, the frequencies are defined as follows:
𝑁 ⋅ 100 𝑁 ⋅ 100 𝑁 ⋅ 100
𝐹1 = 𝐹2 = 𝐹3 = (1)
𝑁𝑋 𝑁𝑌 𝑁𝑍
From Table 2 we can notice that, there is a large number of tweets containing hashtags that
refer to social movements against abuse and solidarity movements for the advancement of
gender equality, such as “#metoo”, “#niunamenos”, “#orangetheworld”, and “#heforshe”. Among
them, one of the most frequent hashtags is “#endsars”, which, as said above, is used for referring
to a series of demonstrations against the police that is accused of torture and other crimes
in Nigeria. However, the use of these hashtags in most tweets shows that these tortures and
crimes also affect Nigerian women. Moreover, we can notice that these hashtags are contained
in approximately 30.82% of the tweets of users who have used at least one hashtag in their posts,
and 22.47% of the entire collection of tweets.
Many of the hashtags used refer to domestic life scenarios, which still today represent an
important social scourge, such as “#relationships”, “#divorce”, “#domesticviolence”, and “#never-
Figure 3: Hashtags used in wishes dataset.
Hashtag Occurrences F1 (%) F2 (%) F3 (%) Hashtag Occurrences F1 (%) F2 (%) F3 (%)
#endsars 375,592 3.56 13.70 9.99 #women 63,063 0.60 2.30 1.68
#metoo 216,588 2.05 7.90 5.76 #niunamenos 59,542 0.56 2.17 1.58
#survivor 194,176 1.84 7.08 5.16 #heforshe 57,912 0.55 2.11 1.54
#neverforget 186,327 1.77 6.80 4.95 #inspiration 51,268 0.49 1.87 1.36
#quoteoftheday 186,220 1.77 6.79 4.95 #mentalhealth 44,849 0.43 1.64 1.19
#children 152,653 1.45 5.57 4.06 #divorce 40,350 0.38 1.47 1.07
#selflove 103,229 0.98 3.77 2.75 #education 37,427 0.35 1.37 1.00
#positivevibes 99,874 0.95 3.64 2.66 #therapy 37,048 0.35 1.35 0.99
#healing 92,818 0.88 3.39 2.47 #domesticviolence 36,875 0.35 1.34 0.98
#love 78,735 0.75 2.87 2.09 #selfcare 36,174 0.34 1.32 0.96
#orangetheworld 75,034 0.71 2.74 2.00 #life 35,900 0.34 1.31 0.95
#quote 71,699 0.68 2.62 1.91 #survivor2022allstar 35,773 0.34 1.30 0.95
#motivation 65,138 0.62 2.38 1.73 #business 35,238 0.33 1.29 0.94
#relationships 64,552 0.61 2.35 1.72 #survivorallstar2022 34,151 0.32 1.25 0.91
#quotes 64,461 0.61 2.35 1.71 #video 34,050 0.32 1.24 0.91
Table 2
Top 30 hashtags in wishes dataset.
forget”. These hashtags are contained in approximately 4.42% of the tweets of users who have
used at least one hashtag in their posts, and 3.22% of the entire collection of tweets. These
preliminary statistics allow us to estimate how much domestic violence and gender inequality
have sparked strong thoughts in people around the world.
3.2. Correlation Analysis
Among the different kinds of correlation that can be analyzed [19], we used Spearman’s Rank-
Order Correlation [20] to evaluate the correlation between hashtags, by applying it to the top-50s
hashtags appearing in at least 30,000 different tweets. The values obtained from this analysis
are comprised in the range [−1, 1]. In particular, given two elements 𝑥 and 𝑦, a zero correlation
value implies no relationship between them, while values −1 and 1 indicate monotonic positive
and negative relationships, respectively. More specifically, a positive correlation means that
the increase of 𝑥 leads to the consequent increase of 𝑦, whereas a negative correlation means
that when 𝑥 increases, 𝑦 decreases. Two elements have a high correlation value if they have a
similar rank, whereas if their rank is significantly different they have a low correlation value.
Figure 4 shows the correlation matrix of the most frequent hashtags used in the tweets of
wishes. As we can see, there is no evidence of strong negative correlations between hashtags.
However, there are some weak correlations between “#children” and the hashtags “#quoteofthe-
day” and “#metoo”. In the first case, the negative correlation is probably due to the fact that the
tweets containing quotes of the day often do not contain any reference to children, but they are
reflexive aphorisms about woman and their role in society. In the second case, the negative
correlation between “#children” and “#metoo” is due to the fact that the metoo movement is
mainly about harassment in the workplace, leading it to be not significantly related to children.
Other significant correlations can be found between “#motivation”, “#mindset”, with “#motiva-
tionalquotes”, and between “#inspiration” with “#inspirationalquotes”. These positive correlations
indicate that many tweets and users provide motivational messages for women living in dis-
tressed family and work situations.
Another topic covered by tweets in wishes dataset regards the relationship between the
#business
#children 1.00
#divorce
#domesticabuse
#domesticviolence
#education
#endsars 0.75
#healing
#health
#heforshe
#heforshexkerem
#inspiration
#inspirationalquotes 0.50
#kids
#lawofattraction
#lekkimassacre
#life
#love
#machinelearning 0.25
#meditation
#mentalhealth
#metoo
#metoogr
Correlation
#mindset
#motivation 0.00
#motivationalquotes
#neveragain
#neverforget
#niunamenos
#orangetheworld
#positivevibes 0.25
#qotd
#quote
#quoteoftheday
#quotes
#relationships
#selfcare 0.50
#selflove
#success
#survivor
#survivor2022
#survivor2022allstar
#survivor41 0.75
#survivorallstar2022
#technology
#therapy
#video
#wapbaze
#women 1.00
#wordsofwisdom
#wordsofwisdom
#machinelearning
#business
#children
#niunamenos
#quoteoftheday
#success
#divorce
#domesticabuse
#domesticviolence
#education
#endsars
#healing
#health
#inspiration
#inspirationalquotes
#heforshe
#heforshexkerem
#kids
#lawofattraction
#lekkimassacre
#life
#love
#meditation
#mentalhealth
#metoo
#metoogr
#mindset
#motivation
#motivationalquotes
#neveragain
#neverforget
#orangetheworld
#positivevibes
#qotd
#quote
#quotes
#relationships
#technology
#selfcare
#selflove
#survivor
#survivor2022
#survivor2022allstar
#survivor41
#survivorallstar2022
#therapy
#video
#wapbaze
#women
Figure 4: Correlation matrix of the top-50 hashtags that appear in at least 30.000 different tweets.
women and their children. In particular, we can notice that there exist moderate correlations
between the hashtags “#children” with “#kids” and “#education” meaning that when tweets
discuss children, it is often women who express thoughts about the relationship of them with
parents and with the school.
Finally, other interesting correlations can be found between the hashtags “#healing” and
“#mentalhealth”, and between “#selfcare” and “#selflove”. These hashtags are contained in tweets
in which many people ask for support and positive opinions for healing from situations of
violence that have indissolubly marked their lives. Moreover, they incite self and free love
without borders, because, in modern society, situations of discrimination and violence capable
of limiting people’s freedom are no longer acceptable.
4. Sentiment Analysis
Sentiment analysis is a field of natural language processing (NLP) that allows to automatically
evaluate user comments in order to determine whether a text expresses a positive or negative
opinion. It is based on the main methods of computational linguistics and textual analysis,
1.0
Positive
0.8 Negative
Neutral
% of tweets
0.6
0.4
0.2
0.0
Turkish
French
Russian
Portuguese
Greek
German
Arabic
Dutch
English
Hindi
Italian
Indonesian
Japanese
Spanish
Figure 5: Sentiments detected for the analyzed languages.
and it is adopted in several sectors, such as politics [21], stock markets [22], marketing [23]
and sports [24]. In our study, the sentiment analysis aims at evaluating the feelings expressed
by users about gender inclusion and the role of women in different countries. In particular,
we performed a multi-language sentiment analysis by considering 3,275,529 tweets written in
Arabic, Dutch, English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Portuguese,
Russian, Spanish, and Turkish languages. The sentiment analysis on all these languages has
been performed using XLM-Roberta [25]. This model is based on Google BERT (Bidirectional
Encoder Representations from Transformers) [26], which is a bidirectional model capable of
identifying the meaning of words by considering only the other words contained in a sentence.
BERT has been designed to be easily fine-tuned for multiple tasks, like language inference and
it has been already trained on a huge number of text in different languages.
Figure 5 shows the sentiment percentages detected for each languages involved in our
evaluation. As we can see, tweets written in Hindi and English represent the only ones where
the percentage of positive sentiment exceeds both neutral and negative sentiments, i.e., 39.32%
and 53.42%, respectively. Moreover, we can notice that for Russian, German, Dutch, and Greek
languages there is a predominance of neutral sentiment and low percentages of positive tweets,
i.e., 9.9%, 18%, 19%, and 12.9%, respectively. Concerning Italian, Portuguese, Indonesian, and
Turkish tweets they present a predominantly neutral sentiment, but the percentage of positive
tweets exceeds the negative ones. Furthermore, we can notice that for Spanish, Japanese, French,
and Arabic tweets the main sentiments is negative. In fact, French tweets have the highest
percentage of negative sentiment, i.e., 51.62%, whereas for Arabic tweets the percentages of
tweets is similar for all different types of sentiments.
Although the previous analysis provides an overview of the sentiments of the tweets for each
language, to analyze the impact of the sentiments in relation to the geopolitical situation of the
various countries, we performed a more detailed analysis of the tweets by taking into account
the geographical origin of the tweets (Figure 6). Positive tweets are shown in green, negative
tweets in red, and in yellow the neutral ones.
As we can see, there is a high concentration of tweets from the American continent, Indonesia,
Central Europe, and India. There is also a relevant number of tweets from China, Japan, and
some African countries, such as Nigeria, South Africa, Kenya, and Somalia. In particular,
the highest concentration of negative tweets is from India, Africa, and some areas of South
America. A possible explanation is that in these countries there is an important part of the
Figure 6: Analysis of the geographical origin of tweets in wishes dataset.
population living in difficult conditions and there still exists a strong disparity between women
and men, compared to more developed countries. In fact, Jewkes highlighted how poverty
and the associated stress are key factors that contribute to violence and that affect women’s
empowerment [27]. Considering the countries with many negative tweets, we can indeed
notice that often the living conditions are difficult. The economic and political situation of
African countries has led this continent to be classified as the last in terms of nominal gross
domestic product per capita, according to the International Monetary Fund5 . Similarly, also
South America suffers from the problem of poverty and it is the second to last continent in
this ranking. Concerning India, overpopulation can be another negative factor, because it leads
to high levels of unemployment and unfair income distributions. This generates stress and
discontent, which can lead to violent behaviors. In fact, as reported from the National Family
Health Survey-5 6 , nearly 30% of women in India have experienced sexual or physical violence.
Moreover, the Women Peace & Security Index 7 have ranked 170 countries based on women’s
inclusion and safety, by taking into account several factors, such as social norms, education
levels, and economic situations. Among all these countries, it is possible to notice that countries
5
https://statisticstimes.com/economy/continents-by-gdp-per-capita.php
6
http://rchiips.org/nfhs/NFHS-5_FCTS/India.pdf
7
https://giwps.georgetown.edu/wp-content/uploads/2021/11/WPS-Index-2021.pdf
like India, Nigeria, Somalia, and Congo are in rather backward positions, i.e., 148th , 130th , 159th ,
and 155th position, respectively. Furthermore, it is important to notice that the presence of the
endsars movement in Nigeria could contribute to more negative tweets, since they highlight the
violence of the police against all the Nigerian people.
Concerning the Central Europe, we can notice that there is a greater balance between positive
and negative tweets. The latter are probably due to the episodes of violence that probably
raised reactions by several negative discussions on Twitter. Instead, the high number of positive
tweets is probably due to the fact that the awareness campaigns and movements, such as MeToo
and HeForShe, are positively impacting the European population. Similarly, for the population
of Latin America, the spread of the awareness campaign of the Niunamenos movement has
probably led to a greater number of positive tweets.
The analysis discussed in this section revealed interesting differences in the sentiments
expressed in tweets with respect to the language and geographic origin of tweets. Unfortunately,
there are still many situations and countries in the world that require more intensive awareness
campaigns on violence against women and in favor of gender equality in order to sensitize
different cultures to the criteria of non-violence and respect for other people.
5. Conclusion and Future Directions
Aiming at analyzing the resonance of topics related to the inclusion and safety of women in
modern society, with this study we collected a huge quantity of tweets concerning gender
equality and domestic violence in the wishes dataset (also shared in a public repository). Then,
we analyzed the collected tweets by highlighting the most used hashtags and the correlations
between them. Finally, we performed a sentiment analysis process in order to emphasize the
positive/negative inflection of tweets with respect to the tweet’s language and/or geopolitical
areas from which they have originated. The performed analysis allowed us to notice that many
of the collected tweets are related to no-profit movements and/or awareness campaigns, which
promote woman empowerment and promptly highlight violent episodes according to events
occurring throughout the world. An interesting witness of the proposed analysis is related to
the tweet concentration (also according to sentiments they include), which revealed a different
perception with respect to the social and political scenario in countries.
Starting from this preliminary analysis, many other deep analysis could be performed. For
instance, it is possible to analyze contents and sentiments of tweets according to features
more related to the user profiles and their influence over social networks. These analyses
could be performed also exploiting topic modeling techniques to more accurately identify the
content of tweets with the aim to obtain a more detailed analysis of them. Moreover, studies
could be conducted by specifically considering events and/or awareness campaigns. Finally,
a comparative analysis could also be accomplished by collecting and considering posts from
other social networks, blogs, and websites [28].
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