Andrea Russo, Department of Physics and Astronomy ”Ettore Majorana”,
University of Catania, Via S. Sofia, 64 95123 Catania (Italy)

# Entropy-rate as prediction method for newspapers and information diffusion

Andrea Russo1-21-2affiliationmark:  Antonio Picone11affiliationmark:  Vincenzo
Miracula11affiliationmark:  Giovanni Giuffrida33affiliationmark:  Francesco
Mazzeo Rinaldi3-43-4affiliationmark:  11affiliationmark:  Department of
Physics and Astronomy, University of Catania, Italy
22affiliationmark:  Department of Sociology ”Newman”, University College
Dublin, Ireland
33affiliationmark:  Department of Political and Social Science, University of
Catania, Italy
44affiliationmark:  KTH, Royal Institute of Technology, School of Architecture
and the Built Environment, Stockholm<EMAIL_ADDRESS>
###### Abstract

This paper aims to show how some popular topics on social networks can be used
to predict online newspaper views, related to the topics. Newspapers site and
many social networks, become a good source of data to analyse and explain
complex phenomena.
Understanding the entropy of a topic, could help all organizations that need
to share information like government, institution, newspaper or company, to
expect an higher activity over their channels, and in some cases predict what
the receiver expect from the senders or what is wrong about the communication.
For some organization such political party, leaders, company and many others,
the reputation and the communication are (for most of them) the key part of a
more and complex huge system.
To reach our goal, we use gathering tools and information theory to detect and
analyse trends topic on social networks, with the purpose of proved a method
that helps organization, newspapers to predict how many articles or
communication they will have to do on a topic, and how much flow of views they
will have in a given period, starting with the entropy-article ratio.
Our work address the issue to explore in which entropy-rate, and through which
dynamics, a suitable information diffusion performance is expected on social
network and then on newspaper. We have identified some cross-cutting dynamics
that, associated with the contexts, might explain how people discuss about a
topic, can move on to argue and informs on newspapers sites.

###### keywords:

Computation social science, Information Entropy, Social media, Newspaper,
Social dynamics.

##  1 Introduction

Nowadays public discourse around public and political events, especially those
that occur online, are increasingly influenced by the issues that media
companies choose to concentrate on R1 . News media not only have the power to
create public awareness around social issues R2  R3 , but they can also
influence how the public perceives what issues are most important R4  R5 .
These dynamics are essential to understanding how people consume news and how
they engage in discussions around specific events. Social media are the most
relevant online communication channels designed for networking R6 . When used
effectively, their applications can promote dialogue R7 , facilitate
information transfer and understanding R8 , engage stakeholders R9 , and
improve communication and collaboration in online environments R10 .
Spreading the right information to the right audience has always been the
underlying goal of proper communication. However, it is hard to estimate
whether the intended addresses of the communication have understood the
information correctly, especially if the process of communication was
irregular and complex and/or the event to communicate is complex. Today, we
believe, it might be possible to assess whether the addressees have received
the sender’s right information by analyzing the related data created on the
various digital platforms, and advising the newspaper itself about the
possible amount of visualization from that specific topic on his site. To make
it clear, when the addresses have received the right information, there is no
need for other information to satisfy the addressees about the event R34 .

From a methodological standpoint, users on social networks often use hashtags
embedded in their discussion. Thus, we use hashtag-related data to estimate
any lack of understanding or curiosity about a particular issue: if people
continue to talk about a specific topic, they unconsciously need better
explanation and information on that topic R11 . This can be measured by
analyzing readers’ activity within a specific time frame.

We believe that is possible to properly measure real-time hashtag trends on
social networks and information entropy-rate —for specific topics— by
informing the addressees about the inferred and perceived information by the
readers.

In general, this is very handy for political communication or business news
when prompt public reactions to given issues are particularly relevant. The
entropy-rate is the hypothetical average level of ”information”, ”surprise”,
or ”uncertainty” inherent in the variable’s possible outcomes, and we use it
to estimate complex processes like the reception of information. We use data
from social networks to study the crucial factors in the communications
process. We measured the natural attention/interest temporal decay for some
topics and used these curves as a benchmark in our study. In general, we
noticed that standard news, such as sports events or flash news, exhibits an
interest natural temporal decay measured by a low entropy value and standard
behavior. Conversely, high entropy values for some topics lead to an unbalance
between intended information (by publishers) and perceived information (by
readers).

We applied our model to different, and sometimes complicated topics, such as
government/institutional communication, Political news, Global news, Disaster
news, Sport news and Daily news. In general, some of those news have been
followed by a low social network activity, but other have shows an intense
social network activity, due to the high entropy about the topic.

This paper aims to contribute to such discussion by using social networks data
through an entropy-based model and computational social science approach. Our
model does not intend to understand the entropic reaction to different
communication channels, but our model is to examine the entropic reaction to
the type of event (standard and equal to all other communication channels),
and predict circa the amount of online traffic on online newspapers.

This paper is organized as follows: Section 2 introduce relative works on
information entropy, Section 3 describes the methodological approach, while
Section 4 presents the case study, Section 5 show the data, result and the
information structure, and finally section 7 conclusions and future work.

##  2 Related works

Entropy-based analysis and computational method have already been introduced
in various prediction scenarios such as atmosphere R30 , network traffic R31 ,
human mobility R32  and Radio Spectrum State dynamics R33 .
Many of these related works focus on public opinion research, influence
cascade, factors influencing diffusion of emergency information, and many
others. Our paper tries to advance the research on the process of information
diffusion, adding newspaper data to evaluate the uncertainty of the
information and the request from people to further/extra information.
We try also, to improve the research on emergency information dissemination
R21  with an entropy classification, enlighten that in the field of social
media research many other topics and events can be studied and kept under
observation, and in some cases be predictable.

Siqing Shan and Xiao Lin R21 , have established how to efficiently evaluate
uncertainty, with the Ya’an earthquake and Wenchuan earthquake, revealing
information’s uncertainty in population. The analysis of relevant blog content
revealed that when the Ya’an earthquake was mentioned, it was often associated
with the Wenchuan earthquake, which occurred five years earlier. Some people
even considered the Ya’an earthquake an aftershock of the Wenchuan earthquake,
thus prompting a revival of the topic of the Wenchuan earthquake.

Yin Jie et al. R22 , have analyzed Twitter messages generated during
humanitarian crises, and focused on the classification of the disaster event
(e.g., traffic accidents and civil disorders). The result showed that more
generic features, such as hashtag count or mention count, were more effective
than incident-specific features, such as actual hashtag or mention values, for
identifying previously unseen types of disasters. Accuracies for the best
setting (unigram plus hashtag count) varied between 60% to 73%. Also they
monitored Twitter for a specific event, evidencing that a large volume of
tweets published every second are considered irrelevant. Even when the tweets
are discussing an event of interest, depending on the application one may be
interested in prioritizing different classes of messages. To address this need
in the context of disaster management, they study three different tweet
classification settings: disaster or not, disaster type, and impact
assessment.

Peng Sancheng et al. R23  presented a framework to quantify social influence
in mobile social networks. The social influence of users was measured by
analyzing the SMS/MMS-based communication behaviors among individuals. In
addition, they revealed and characterized the social relations among mobile
users through the analysis of the entropy of friend nodes and the entropy of
interaction frequency. The analytical results show that the influence spread
of their proposed method outperforms that of the random method and that of the
degree-based method. Also, they show how only the top 1 to 5 nodes are
influential nodes and the succeeding nodes do not contribute to increasing the
influence spread.

Kolli et al. R24  have studied and quantified the interplay between the
regular (and thus predictable) and the random (and thus unforeseeable)
underlying online social media cascade dynamics. Their research aims to apply
a framework for characterizing the predictability of cascade trajectories and
the theoretical “maximal predictability”. They show that at least 20% of the
time the cascade volume changes in a manner that appears to be random, and in
the remaining 80% of the time it is possible to predict the cascade’s future
volume.

Borge-Holthoefer et al. R25  have used entropy analysis on information-driven
dynamics. They show up that in some cases, discussions evolve organically,
building up momentum up to the point where the exchange of information is
generalized; however, in some other cases, the discussions emerge suddenly as
a reaction to some unexpected external event. They work focusing on protests
or breaking-political events (Brazilian protests) or public debate.

Barros et al. R27  propose a novel method to detect events on Twitter based on
the calculation of the entropy of the content of tweets in order to classify
the most shared topic as an event or not. Their goal consists of capturing the
phase transition of entropy when it changes to a certain value. They
hypothesized that during the occurrence of an event, the entropy of the
bigrams extracted from the social media changes its dynamics, they observed a
continuous phase transition of the entropy dynamics. Furthermore, this
provides some evidence that their method is sensitive to detecting events that
occur in a short time. He obtained data from a football match, where breaking-
event (penalty, yellow or red card) can appear suddenly provoking reactions on
social networks.

Park Han Woo R28 , has proposed negative entropy not as a comprehensive or
representative index of elections but as an experimental and innovative
measure for events occurring in social media environments. The research
question was 1) What (social) media are more likely to generate (negative)
entropy across different periods? ; 2) Which politicians (or pairs of
politicians) are more likely to generate (negative) entropy for bilateral,
trilateral, or quadruple relationships across various media and periods? He
examines the information entropy produced by various types of web-mediated and
social media platforms before the election, particularly with respect to
presidential candidates. Twitter showed the greatest negative entropy,
followed by Facebook and Google, in that order.

Senevirathna et al R29 . present a novel method for tracking influence
relationships over time and linked influence cascades. Focusing on
cryptocurrency communities, they show that user distributions over influence
cascades for the cryptocurrency community were robust across platforms, while
others community were more platform-sensitive. They evidence that influence
cascades are typically analyzed using a single metric approach (all influence
is measured using one number). However, social influence is not monolithic;
different users exercise different influences in different ways, and influence
is correlated with the user and content-specific attributes. Also, they
evidence how influence was propagating within the cryptocurrency community. On
both Twitter and GitHub they were similar, with the exception of contribution
and sharing events. Furthermore, there are no similarities in how influence is
propagated when comparing the two communities on either platform (Twitter and
Github). In sum, they illustrate how to entropy can be used as the measurement
of influence to estimate the degree to which causal relationships existed
between user actions, and they do not find any individual influence
relationships across the two communities on Twitter that show significantly
similar progressions of the magnitude of influence over cascade level. R29

##  3 Methodological approach

To evaluate and predict information dynamics on social networks and
newspapers, we used several tools to justify the data quality and the dynamics
on different platforms. In detail, we have described the tools and their goal
in Table 1.

Tools | Goals
---|---
Twitter API | Collect data from Twitter
Shannon Entropy | Evaluate High or Low entropic events
Info structure | Analyse data sample
Scraper | Collect data from Newspaper
Data-viz | Visualize data-patterns
Odds-ratio | Correlation between topic and articles
Table 1: Tools & Goals

###  3.1 Twitter API

The Twitter API enables programmers and researchers access to Twitter elements
like Tweets, Direct Messages, Spaces, Lists, users, and more. We have collect
tweets related to various topics, by using Tweepy and the Twitter archive API.
Both services use permission from Twitter to obtain and gather data, but any
downloaded topic needs revisions and a cleaning process to increase the
quality of the research. For example, we found many copy-paste tweets (caused
by spamming process, or fake-account/bot), and also several tweets had
repeated questions marks, they were thus removed. For any topic, we use the
same methodology to obtain standard and quality data. This is request also
from the entropy calculation because it is essential to know every evolution
of the topic in every hour. In addition, to obtain the correct amount of
tweets (defined as the number of tweets) for each day/hour we use
getdaytrends.com, a specific site where it is possible to monitor every topic
in real-time and also aged topic. Also, the Topic selection process, as
follows various information categories from newspapers. We select
specifically: Political topics, World News, Sports News, Cultural news, and
City news. In total, our data count more than 20.000 tweets.

###  3.2 Shannon Entropy

We used Shannon entropy, to evaluate and quantify the degree of information
assimilation from people about topics. In information theory, the “importance
of the information” contained in a message is directly related to how “hits”
or “sudden” the message is for the reader R12  R13 . Suppose we have a biased
coin with probability $p$ of landing on heads and probability $1-p$ of landing
on tails. For what value of $p$ do we have the maximum ”surprise” or
”uncertainty” on the outcome of a coin toss? If $p=1$, the outcome of a coin
toss is expected to get always head, so there is no surprise or uncertainty.
Similarly for $p=0$, when we always expect the coin to land on tails. If
$p=0.5$, then we have the maximum surprise or uncertainty. This value of hits
or sudden has been mathematically proposed by Claude Shannon R13  in 1948 as
part of his theory of communication. After a discussion with John Von Neumann,
Shannon decided to use the term ”entropy” in place of the word ”uncertainty”.
At a conceptual level, Shannon’s Entropy is the ”quantity of information” of a
variable. This turns into the amount of memory (e.g. number of bits) required
to store the variable, which can be understood as the amount of information
contained in that variable. Shannon’s entropy is expressed by a number and the
calculation is straightforward; in particular, it is not the number of bits
needed to represent all the different values that a variable could take, but
it is just the raw data.

Shannon’s intuition was that ”less storage might be sufficient to store the
information”. Shannon’s entropy metric helps to identify the amount of storage
space needed for information. An alternative way of looking at entropy is
therefore as a measure of the ”compressibility” of the data, that is, a
compression metric. However, how far can the raw data be compressed without
losing the information?

Formally, the entropy of our biased coin is given by:

$H(coin)=-(p*log(p)+(1-p)*log(1-p))$ (1)

and the base of the logarithm can be chosen arbitrarily. When the base is 2,
the entropy is measured in bits. If instead the base is e, the entropy is
measured in nats. Finally, if the base is 10, the entropy is measured in dits.

When $p=0.5$, $H(coin)$ is maximal, and it is equal to 1 bit. When instead
$p=0$ or $p=1$, $H(coin)$ is minimal, and it is equal to 0 bits.

The concept of entropy can be generalized from the simplest case of a coin to
the more complex case of a discrete probability distribution. A discrete
probability distribution over $n$ possible outcomes $x_{1},...,x_{n}$ is given
by $n$ probability values $p(x_{1}),...,p(x_{n})$, where $0<=p(x_{i})<=1$, and
the sum of all $p(x_{i})$ is equal to 1. Note that a coin is a probability
distribution over two possible outcomes.

Formally, the entropy of a discrete probability distribution is defined by:

$H(P)=-(p_{1}*log(p_{1})+...+p_{n}*log(p_{n}))$ (2)

where, as before, the base of the logarithm can be chosen arbitrarily.

A clearly communicated concept does not lead to a “surprised” or “unexpected”
reaction: its information value is low, which corresponds to a low entropy
level. Conversely, a poorly communicated concept leads to high entropy (i.e.,
a more surprising effect) on the readers. A poorly understood message triggers
a sudden need in the reader for additional information for clarification and
better understanding, for example, students asking for more information and
clarification to understand complex topics.

In today’s digitized world, this information request produces a hype of
digital activity such as social network discussion, Google searches, newspaper
consumption, etc. Thus, by real-time measuring such digital activity, we might
estimate the entropy of a specific topic and, this allows us to recognize its
efficiency. Assessing the information diffusion quality of a certain topic
with the entropy-rate depends on the information entropy level of the message
which, we show, is directly related to the addressees’ reaction to the
message. We measure addressees’ responses by analyzing social media data (in
the same period) connected to that topic.
We believe that if a topic is well-communicated, people do not (necessarily)
need to comment about it on Twitter or, at least, no more than usual.
Otherwise, as mentioned above, if the message was unclear, people will look
for better understanding by twitting comments and expressing opinions on the
Web. The more social media activity on that topic we measure, the higher
entropy for that topic is.
For the purposes of our analysis, we distinguish two types of events:

  1. 1.

Low entropy events (LEE)

  2. 2.

High entropy events (HEE)

LEE are events whose, the information diffusion was quickly understood by the
public. In general, immediately after news on this type of event appear either
in newspapers or on TV, there is a hype on social media activity. LEE social
media-related activity usually decays almost entirely in 4 hours. This holds
true in general for many easy-to-understand topics like sports events, flash
news, natural routines, etc. Information on such types of events shows low
entropy levels and standard behavior. Conversely, HEE are more complicated
events. Information dissemination of these events is inherently more difficult
compared to LEE. Social media activity for this type of communication follows
an entirely different pattern. We measure a decay curve over a 24-72 hours
period. During this time, an intense communication activity takes place on
social media. HEE exhibit a higher entropy level in general as their
communication complexity is higher. After that time, people tend to stop
discussing and commenting on those. In Figure 1 we show the different trends
for LEE and HEE (Thousands of tweets).

Figure 1: Entropy events (LEE) vs High entropy events (HEE) median for 72
hours and relative tweets

We have identified and analyzed many different topics-spawn events on social
networks like everyday events (sports, weather, etc.), cyclic-time events
(Mondays-motivation, etc.) as LEE, and unpredictable events like uncommon
events (an important unexpected murder; e.g., Jeffrey Epstein), and
uncommonness outcomes events as HEE (see fig. 1). High entropy information
produces a better information diffusion than Low entropy one; but also, people
would have difficulties understanding it. In contrast, Low entropy information
requires less effort to be understood and hard effort to be spread. These
events’ natural decay shows a complicated and long curve that grows and slows
down during day-nighttime, but it stays a trending topic for at least 60
hours. Other natural decays show fewer dynamics and more predictive behavior.
HEE remains rare, but they indicate the unbalance between the senders’
information requested and the information supply. How is it possible to
estimate if something is wrong, and how is it possible to expect high entropic
events?

The time when everyone listens and tries to assimilate the information is a
crucial stage because if the communication goes well (sound information
diffusion and right information received), there is no HEE. Still, if the
message/information’s entropy is high, the HEE arises (1). After that,
usually, people start to debate and talk on social networks and (or) in our
private circle (family, friends, etc.). At this point, if enough people start
to speak and the topic became a Trend topic for a long time (at least 40
hours), we can evidence how much the information diffusion was enlarged and
how much the information was clear (2). At some point, the organization that
has provoked the HEE will dispatch the correct definition of the information
sends (3).

###  3.3 Information structure

The Information Entropy of a message can be improved with an information
structure method. To prove if the HEE are more entropic and consequentially
need more information to be assimilated, we have chosen to analyze the syntax
structure sample from Twitter. Other papers evidence a significant change
related not only to the entropy rate, but also to the information structure on
social networks.
The Arabic Newspapers work R18 , had tried to recognize significant changes in
the social state via changes in the linguistic performers of the media.
Entropy rate, and the information structure about Hashtags on social networks
can also describe cultural and socials dynamics, also adding information
request coming from society. This data also suggests a strategic way to
improve the diffusion of the information; the entropy rate of some information
(especially institutional and political information and/or in emergency case
R20 ) should not be at the medium between HEE and LEE, but tactically it
should be slightly above the average, so as to be compressible to many people,
and at the same time, alluring, because the average information spread is more
entropic. Also, Minkyoung et al. (2013) R19 , have studied the dynamics of
information diffusion across social networks, confirming this logic, showing
topics with controversial subjects, which seem to lead to longer discussions
representing personal opinions, like the HEEs case.

###  3.4 Scraper

We use a homemade scraper to collect data from newspapers like articles, name
of the article, the text of the article, and numbers of visualizations by
hours/days. Those data give us the resources to quantify the interaction and
the dynamic between readers and the most relevant topic of the day.
As for the API methodology, we have collected, cleaned, and supervised the
data to increase the quality of the data and research. The revisions and the
cleaning process has taken some time, due to the unstructured data gathering.

###  3.5 Data-viz

Data-viz isn’t a single tool, but a collection of tools to elaborate data and
visualize them. With data visualization, it is possible to translate
information into a visual context, such as a map or graph, to make data easier
for the human brain to understand and pull insights from.

We have used many different simple or complex data visualization tools, but in
the end, we used the simple and original template of the data visualization
tool available in Excel.
Thanks to the data collected from Twitter API and the Scraper, we merge the
data to observe oscillation and information dynamics between Social networks
and newspaper digital platforms.

###  3.6 Odds-ratio

The odds ratio is a statistical measure that shows the degree of correlation
between two factors. Odds ratios are used to compare the relative
probabilities of the event of interest, given the exposure to the variable of
interest.

The odds ratio helps to identify the probability that an event ’A’ leads to a
specific event ’B’. The higher the odds ratio, the greater the probability of
the event occurring with the exposure. Odds ratios of less than one imply that
the event is less likely to be related.

##  4 Data & Case study

During 2020, given the high amount of atypical events, we have collected many
Tweets related to various topics. We have used Shannon’s Entropy to evaluate
the request for information from people, Table 2 shows the HEE and LEE between
January 8th 2020 to November 1st 2020. Meanwhile, Figure 1 shows the average
time dynamic activity between HEE and LEE. 111”Times by hours” define how many
hours the topic is ”alive” since his first apparition on social network and
newspaper;
”Activity on internet” define how many tweets or visualization are related to
the specific topic. Obviously, the total amount of tweets is bigger then the
newspaper visualization, so we had to reduce by 10, to simplify and ease the
socialnetwork-newspaper dynamic.
By real-time measuring information entropy on social media, we could promptly
get informed how easy to understand a certain communication was. And we
believe this may have various useful applications in real life.

Table 2: Information Entropy and average tweets/hours. Type | Topic | Tweets | Hours | Entropy
---|---|---|---|---
HEE | DPCM (Apr 26th) | 894.500 | 78 | 5.6113
HEE | BLM | 8.4 M | 127 | 5,5760
HEE | Beirut | 3.2 M | 69 | 5,1962
HEE | Debates2020 | 3.7 M | 47 | 4,8745
HEE | Megxit | 52.100 | 63 | 4,3127
HEE | DPCM (Oct 25th) | 618.150 | 42 | 4.1972
LEE | Int.WomensDay | 1.5M | 21 | 4,1457
LEE | ValentinesDay | 812.900 | 34 | 4,0572
LEE | Tokyo2020 | 3.1 M | 24 | 3,6664
LEE | SuperLega | 45.400 | 27 | 4.0541
LEE | G.dellibro | 11.600 | 14 | 3.8232
LEE | JuveInter | 48.500 | 16 | 3.6126
LEE | MilanNapoli | 16.800 | 10 | 3.5672
LEE | DPCM (Oct 13th) | 26.700 | 12 | 3.1569
LEE | Dupasquier | ¡10.000 | 7 | 3.0201
LEE | GazaU.Attack | 623.800 | 8 | 2.9740
LEE | ObiWan | 20.300 | 7 | 2,6907
LEE | SuperLeague | 616.700 | 48 | 2.5417

We have chosen to study the HEE because they are not only demonstrating higher
activity in social networks but also because there is significantly more
discussion (debates) on HEE topics than LEEs. In this chapter, we would make a
brief description of the HEEs chosen for our work.

Figure 2: Twitter data trend comparison.

###  4.1 Congiunti & DPCM

Congiunti and DPCM was two important topic and hashtag during the Italian
first Covid wave. The hashtag respects the two phases of the Covid pandemic,
the discovery, and the adaptation.

On April 26th 2020, after three months of lockdown, Italians were anxiously
waiting for government news about legal procedures to finally meet parents,
relatives, friends, and significant ones. DPCM is the acronym for ”Decree of
the President of the Council of Ministers”, and it was a legislative way to
adapt complex organizations (like government, society, and institutions) to
adapt and defend themselves from the Covid pandemic. The DPCM’s guide from the
government wasn’t clear enough, the government had used too specific or
unknown words for described the ending lockdowns procedures.

The #Congiunti was indeed the core problem of the information dissemination,
because in Italy “Congiunti” (“joint people” in English) does not have a legal
definition, so it was interpretable in many ways, like: can an unmarried
couple be considered “Congiunti”? What about family members not living
together? What about second/third cousins? What about a son/daughter living in
separate cities? What about a couple of the same sex (formally cannot be
married in Italy). And the list goes on. The term shows the highest entropy
value, and Italians were largely confused about the word “Congiunti”, and its
meaning was essential to properly understand the restrictions. Five days after
the official announcement, the Italian government published some FAQs to
clarify the meaning of the term “Congiunti”. As already mentioned, the
“Congiunti” case of April 2020 was not the only one we observed and measured.

However during the second wave of Covid, another #DPCM on October 25th 2020
has brought attention to Italian society, This new DPCM was defining a three-
tier system based on a color code according to the intensity of the epidemic
danger for the different areas in Italy. This measurement was established to
adapt the society and the public administration to reduce the Covid diffusion
in different areas, avoiding another hard lockdown procedure. Each of the 20
Italian regions was assigned a different color: Red (high-risk), Orange
(medium risk), and Yellow (low-risk) zones. Each color defines a specific set
of specific restrictions. Italians were confused about which color their
region would become, causing limitations to meeting people or social
confinement.

The entropy of this topic became high due not only because of knowing the
color of your own region, but also to understand the parameter about how this
limitation was implemented. Was more important to determine the colour, the
amount of daily Covid cases, or the amount of daily free hospital seats?

###  4.2 Beirut

On 4th August 2020 a large explosion occurred where more than 215 people were
killed and about 7,000 people were wounded. All this left the Lebanese capital
in ruins. All the world started to wonder how in 2020 that was possible so
they started to write on social networks all the possible theories they could
think of, this is one of the main reasons why we found an high entropy rate,
as people were constantly seeking for new information.

Many Lebanese people blame security and political officials who failed to
enforce safety regulations but until now no one has actually held responsible
for that. All we have up to this moment is that the ammonium nitrate was
improperly stored at the port of Beirut. All this led to huge protests not
only in social networks but also in real life, where what began as a protest
outside the Palace of Justice turned into heavy gunfire on the streets.

###  4.3 Debates

The 2020 United States presidential debate was defined by various newspapers
like an ”unwatchable”. The first debate, scheduled on 29 September 2020 was a
mess for the chaos it was generated as Trump turned it into chaos as he
repeatedly talked over Biden and attacked his Democratic opponent. United
States citizens have discussed who was the winner in the midst of all the
chaos, and all this situation generated - once again - a mediatic effect and
an high entropy rate as irony and anger by people arose in social networks as
they were also powered by the classic social media, for example the ABC’s
journalist George Stephanopoulos define it as the ”worst presidential debate I
have ever seen in my life”.

The second debate was scheduled for 15 October 2020, but was cancelled due to
Trump testing positive to Covid-19. The reaction about this matter on social
networks was hilarious as people made fool of Trump saying - someone
ironically - that he could not stand the debate so he pretended to be
positive. The finale debate was scheduled for 22 October 2020 and was - once
again - a mess. The debate has been marked by Trump’s constant interruptions
and insults. For the 60% of viewers it was the most bottoming out moment of
the American political history.

###  4.4 Black lives matters

On 25th May 2020, George Floyd was murdered by a police officer in
Minneapolis, Minnesota. When Floyd was killed, the city became the epicenter
of riots. After his death, protests and violences spread out quickly across
the United States, against the police. The death of George Floyd gave rise to
a series of protests and civil unrest against police brutality and racism.
Protests quickly spread nationwide in support of the Black Lives Matter (BLM)
movement.

The movement has been around since 2013, but the death of Floyd arise the
protest and the sentiment against police. BLM is a community organising, which
starts with the support of local communities. Not having a leader means that
BLM is at the mercy of disorderly outbursts of social anger: it is organised
through a decentralised and horizontal structure. Social media played an
important role in the growth of BLM, especially at first.

We detect an high entropy-value because people wanted to know the motivation
for that police violence. Not only for the George Floyd case, but also
violence in manifestation. Social media also proved to be much more than an
efficient tool to spread the message: they were a way to deepen people’s
understanding of structural racism by showing connections between seemingly
unrelated incidents.

##  5 Result & Newspaper metrics

Newspaper industry has been changing it is profile during the last decade. As
it is still an industry we need to make it clear that they base their main
goal is to make money by writing and selling news. For various years this was
achieved by being politically polarized and receiving money incomes from both
the political sides and the advertisement on the newspapers themselves. While
this is still true, we are assisting to a gradual shift of main revenue from
the classic paper to the new way news are distributed: online newspapers offer
way more information and in a quicker way, for example it was necessary for it
to be physically printed daily while this concept is different, there is
always a no-stop flow of contents that can be added anytime. Advertisement is
now done in a digital way and - usually - as they get a revenue for every
viewer that visits their links they tend to write catchy titles that will try
to catch as much readers as possible. This new way of sharing news is not only
useful for final readers but it also allows social researchers to study a
phenomenon in an easier way, as thanks to scraping and API techniques, it is
possible to extract and analyze them by using digital approaches, reaching the
goal to understand social and information dynamics, like HEE and LEE.

###  5.1 Information time-patterns during DPCM’s events

During the 3 months of lockdown, Italian people waited for the government news
about the procedures to meet parents, friends, and other people lockdown. We
observed that many days before the government’s communications, this topic was
already consulted in newspapers, we checked it on ”Il Fatto quotidiano” and
his site ”ilfattoquotidiano.it” (most consulted online newspaper in Italy in
2020).

Data from newspapers shows that there it was a “social hype” about what kind
of procedure was followed after the lockdown. In the days before the ending
pandemic procedure, we observed that many people looking for information-topic
(Figure 3), and the goal of senders (newspapers) was to dispatch the right
message to most possible people. As already prove in the last section, during
the institutional communication, some words emitted have a high entropy level
and it was difficult to understand properly.

Figure 3: Consulted news Ilfattoquotidiano.it on 26 April 2020

In Figure 4, we merge this schema with the data gathered from newspapers and
social networks, it is possible to infographic what happened on 26 April 2020.
The government announcement started at 20 PM, and at this point news (about
the interesting topic) in newspapers stop to be seen. It took only 5 days for
the government to define what Congiunti means.

Figure 4: Newspaper activity and SN - 26th April

Figure 4 shows the activity from social networks and newspapers about the
Congiunti topic (DPCM of 26th April). Early activity on social networks arises
before the conference (time 0), evidencing a social hype and huge request from
the incoming news related to the incoming information event. In this case, the
social Hype is due to the request for information about the end pandemic
procedure. The Activity on the social network is the Twitter trend data for
#Congiunti Hashtag between 26th April until 1th May 2020, for a total of
$\sim$894.500 tweets. The Newspaper activity is the visualization of the
ending-lockdown-procedures articles from the newspaper-site
”ilfattoquotidiano.it”, between 26th April until the 1st May 2020, for a total
of $\sim$601.930 visualization. Unfortunately data from ilfattoquotidiano.it
doesn’t count the refresh of the page from the same unknown account.

From the newspaper’s point of view (in figure 3), the 38,62% of the
visualisation on the newspaper coming before the Government communication.
Before this moment, all the visualization rose only to understand the
information shared by the government, and the news procedure for the summer
holidays. And in fact, in Figure 4, the curve shows a ”social hype” from
peoples for the imminent lockdown-ending-procedures.

Figure 4, show also a curious dynamic from the newspaper’s perspective. At 10
o’clock we can see a starting big blue curve that emerges after some hour of
inactivity (given by night time). This blue curve arise due the night before,
the Italian Prime Minister Giuseppe Conte, held a talk (on TV) to the nation,
to inform them about the misunderstanding or disinformation about the incoming
national procedures after lockdown. The red line (activity from Twitter)
didn’t fall during the night, due to the discussion on TV from the Prime
Minister, but also because people on the social networks have still continued
the discussion about the unclarity of the message from the Prime Minister. The
joint people-case didn’t disappear early soon, for many months TV-program and
many political talk-show have used the word ”congiunti” as a keyword to
describe the interaction between people from outside family members, and the
possibility to transmit the Covid-19 virus.

Figure 5: Newspaper activity and SN - 25th October

In Figure 5 we show the activity from social network and from newspaper about
the DPCM topic (DPCM of 25th October). This event don’t show any early
insight, because on the October 2020 the Italian government publish many DPCMs
with very small result to reduce the imminent second wave of Covid pandemic.
An early insight arise when big change in society are await. But if the
expectation for the big change is delayed many times, people stops to bring
attention for any news. Some TV program called the publishing of many DPCMs as
”born already dead” laws.
However, the activity on social network is the Twitter trend data for #DPCM
Hashtag between 24th October and 26th October 2020, for a total of
$\sim$618.150 tweet. The Newspaper activity here is still focused on the early
communication process before the other HEE event, showing that people are
interest on the topic. They asking information and be informed about the
standard parameter to the local regional restriction (as already described on
Case study chapter). The visualisation about the article DPCM between 25th
October and 28th October 2020 are $\sim$607.725. As Figure 5 show there is an
high density of people reading news in a shot time (8 Houres), as the 20.39%
(123.930) of the total. But the must major of readers arise just after the
midnight, we count 206.041 visualisation, as the 33.90% of the total, but
distributed over a longer period of time (8 hours for the first, 12 hours for
the second - relatively 8H and 28H-44H).

In both case, we saw after 24 hours a total reduction of visualisation on
newspaper and the arisen on activity on social network, proving that the
information send by the government wasn’t clear enough to satisfy the
information request from people.

Thus, we think that the 12 hours of reading by people, is due to the people
who wanted to carefully understand what they could do in the various limited
zone (Yellow, Orange and Red zone). For example understand if it is possible
to go out of the region, meet some friend or parents, or go to a restaurant or
drink outside.

As shown in the Figure 6, they are taken into consideration 69 hours (starting
from 6 PM o’clock) since the start of the event: during the events occurred in
Beirut it can be seen that during the first 7 hours there is almost the same
activity in both the newspapers and Twitter, this demonstrates that people
were constantly looking for information about the matter. There is a gap of
Twitter coverage between the hour 19 and the hour 33, during the same period
two huge spikes were registered on newspaper instead. After hour 33 we see no
coverage anymore in newspapers, the only events registered after that time is
on Twitter, because people were commenting about the previous news on
newspapers. As we can see the event started to decline reaching its minimum
point in hour 63. There are some points that can be noticed in the Figure 6,
there is a large activity on newspapers that matches the lunch (first spike)
and the morning after the event occurred (second spike).

Figure 6: Newspaper activity and SN - Beirut

Instead, for the topic Debates in Figure 7, we can see a greater activity on
Twitter than the newspapers, nevertheless they show a linear degree growth
that once it reaches its peak it suddenly decreases up to settle to an average
value. The activity on Twitter, instead, follows a different pattern, it is
way more bound to the topic interest. We can notice some fluctuating peaks
until the hour 33, moment since people on Twitter lost interests on the Debate
topic.

Figure 7: Newspaper activity and SN - Debates2020

Eventually, the topic BLM is perceived way more by the online community than
the newspapers. In the first hours of the event show an intense activity on
Twitter reaching a maximum peak of about 83k; newspapers - instead - tend to
delay the beginning of the focus, it was registered on them only after 40
hours that the event was already being discussed on Twitter. Systematically,
it is preferred to search for this kind of info on Twitter than the
newspapers, as they never reach the same level of activity of Twitter but only
about the hour 94 after the start of the event as shown in the Figure 8.

Figure 8: Newspaper activity and SN - BlackLivesMatter

We can clearly notice that after a red peak there is an increasing number of
readers that is translated in an increased number of views online. These peaks
can also be seen more than once during the same day for the same event,
highlighting a possible correlation between 1) peaks of tweets; 2) need for
additional info (people move to newspapers to get informed); 3) growth of
number of views of the online newspapers.

###  5.2 Dynamic prediction about newspapers

HEE are very rare events, but otherwise, they show the unbalance between the
information requested and information supplied by the senders.
How is it possible to estimate if something is wrong during the information
communication? and how is possible to expect High Entropic Events and predict
online visualization on newspaper? We have enumerate the various action steps
in information dynamics, trying to highlight the crucial moments and which
moments may be most significant for prediction the online activity for
newspaper and social network.

  1. 1.

If the information request is really important or awaited, it usually shows
some insight before the moment that provides that information needed

  2. 2.

The moment that everyone listens and tries to assimilate the information, is a
crucial stage because if the communication goes well (good information
diffusion and right information received) there is no HEE (only LEE), but if
the entropy of the message/information is high, the HEE is arisen

  3. 3.

After that phase, people usually start to debate and talk on social networks
and/or in their private circles (family, friends, etc..). Thus, at this point,
if enough people start to talk, the topic grows and becomes a Trend topic for
a long time (at least 40 hours). At this point, we can evidence how much the
information diffusion was spread and how much the information was clear

  4. 4.

The newspaper’s goal is to make money by selling news. The newspaper
strategically must talk about that topic and they start to write some articles
to supply the right information, but if the information’s entropy was too
huge, this also affects the newspapers, and they cannot supply the right
information (because they also didn’t understand the information because was
to entropic or because the topic is too complex, and it required time to be
assimilated). People usually continue to talk for at least 2 days

  5. 5.

If was an organization that shared the information, at some point, the
organization that has to provoke the HEE, should dispatch the right definition
of the information sends

The rise and climb in any HEE figure, enlighten the hypothesis of some time-
pattern during the case-study event, and by reaction, a possibility to
quantify and predict online visualization between social network and
newspaper. After the big peak activity on social network, a peak activity also
pops up in newspapers after a short time. This dynamics is enlighten in every
HEE, but we didn’t found the same dynamic from LEE. To quantify the
correlation activity from social network and newspaper we have calculate in
Table 3, how much time in average the first article as been create after the
peak on social network, and how much time take the newspaper to obtain a huge
amount of visualization to the article.

However, for some topic like BLM or Beirut the average is higher then the
others topic, because of the time zone where the events occurred. That means
that if the event arise in the United state, people in EU are still sleeping,
by consequence it hard to writhe and diffuse online news. Indeed, people
usually read newspaper in early morning or during the afternoon for inform
about some specific news; moreover, about LEE, the activity on social media
arise in early morning because is related to specific news of the day
(#giornatamondialedellibro, #mothersday), or also when some specific event
arise (#GazaUnderAttack, #Superlague, footballs match and others). Another
important piece of information that newspapers can obtain, from the HEE
prediction forecast, is the number of newspaper articles. Still in Table 3, we
show the higher net value of number of HEEs articles than LEEs. HEE events
find more articles in newspapers (to explain the event and/or its evolution),
while LEEs have circa only one article.

Table 3: Difference time between: Twitter-article and mayor increase by hours.

Beirut | BLM | DPCM (Apr) | DPCM (Oct) | Debate | TOT
---|---|---|---|---|---
3.33 | 7.75 | 3.75 | 1.25 | 3 | 4,77
1.67 | 3 | 2.75 | 1 | 2 | 2.60

Entropic event | Average articles
---|---
HEE | $\sim$ 6.4
LEE |  $\sim$ 1

###  5.3 Information structure

During the data analysis we also observed a different structure of the
messages R26  relative to HEE and LEE. We observed that HEE have on average
more question mark then LEE, consequentially to proof this result we research
and gathering data from others HEE and LEE (#MilanNapoli,
#Giornatamondialedellibro, #Gazaunderattack etc..). As Table 4 illustrate, HEE
response to the events are structurally different from LEE. The average
indicates the probability of any tweet to contain a question mark. The data
suggest a different information structure about the social reactions to some
topic, proving that HEE and LEE are quite different not only in the ”natural
decay” of the topic, but also in the reaction structure.

Table 4: Question marks on HEE and LEE.

Topics | Tweets | Q. marks | Events | Average
---|---|---|---|---
Debates2020 | 868 | 210 | HEE | 24.2%
DPCM (Apr 26th) | 2000 | 312 | HEE | 15.6%
Megxit | 115* | 17 | HEE | 14.8%
BLM | 1612 | 154 | HEE | 9.5%
DPCM (Oct 25th) | 2000 | 178 | HEE | 8.9%
Beirut | 724 | 53 | HEE | 7.3%
Valentinesday | 1600 | 251 | LEE | 14.25%
JuveInter | 1000 | 71 | LEE | 7.1%
G.dellibro | 1000 | 69 | LEE | 6.9%
SuperLega | 1000 | 68 | LEE | 6.8%
Int.WomensDay | 1000 | 64 | LEE | 6.4%
MilanNapoli | 1000 | 61 | LEE | 6.1%
DPCM | 1000 | 60 | LEE | 6%
SuperLeague | 1000 | 55 | LEE | 5.5%
MothersDay | 1000 | 31 | LEE | 3.1%
GazaU.Attack | 1000 | 14 | LEE | 1.4%
Dupasquier | 1000 | 14 | LEE | 1.4%

Entropic event | Average
---|---
HEE | 13.38%
LEE | 5.90%

As shown in Table 5, five semantic areas were identified. Keywords were
identified for each area. We have use Odds-ratio to prove if there is a topic
connection between the topic from social network, and online news from
newspapers. The methodology of the odds-ratio, needs to clean and set the data
in a good condition for the analysis.

Table 5: Compatibility between Tweets and Words.

Topics | N. of articles | Odds
---|---|---
Congiunti | 8 | 0,132
Beirut | 3 | 0,081
Debates | 2 | 0,063
BLM | 3 | 0,047
DPCM | 3 | 0,031

Average Articles | Average Odds
---|---
$\sim$ 3.8 | 0,071

For example, due to grammatical reasons, documents are going to use different
forms of a word, such as ”organize”, ”organizes”, and ”organizing”.
Additionally, there are families of related words with similar meanings, such
as democracy, democratic, and democratization that share the same prefix.

To reduce the dimensionality of the words, we performs a lemmatization
analysis, that is a text normalization techniques in the fiels of Natural
language Processing that are used to prepare text, words, and documents for
further processing (i.e. the lemma of ”are” is ”be”, the lemma of ”debates” is
”debate” and so on). Afterward, we cross-checked words in articles and tweets
by counting the occurrences for each one of them in both of the sources. This
allowed us to calculate an article/tweet compatibility index for each topic.

Finally, we calculate the odds ratio in order to evaluate the correlation
between articles and tweets. We found that, for the equal N. of articles, some
topics show more interest in online communities pushing users to discuss it on
online platform.

##  6 Conclusions

In a world where the communication is becoming faster and faster, also thanks
to the use of the ICT technologies, social media are widely used for
communicating information of various kinds R14 .

Blogs, social networks, and online newspapers are now social relationship
tools and they build weak and strong ties. In the traditional world,
newspapers simply had to give out information, and people would consume it by
reading or looking at it without interacting. All social media are not only
used to communicate but they are a new way to stay informed and share thoughts
about anything, like if you were in a public place talking to someone.

Simply making information available is not enough for today’s public. Today’s
audiences expect to be able to choose what they read, and most believe they
should be able to contribute content and opinions, too. In particular, the
one-to-many nature of Twitter created an opening for governments to
disseminate relevant messages R16 .

When news breaks – as in our case study – people increasingly turn to the
Internet. Social media platforms became powerful tools for communicating
rapidly and without intermediary gatekeepers. As we have observed, the quality
of information communicated through digital platforms is essential to prevent
misunderstandings and confusion about the message.

The more controversial a topic or message is, the more significant people’s
reaction on social media and the higher the possibility that, as a result,
misinformation will be created on the subject. We introduce an entropic model
allowing researchers to:

  * •

catch how much people have understood about a relative news on social network

  * •

discover a different information structure about different reaction by people,
and different ”natural decay” of the information

  * •

proving better strategy and tactic about information diffusion

  * •

showing high entropic social event in society

  * •

to transmit an information at an optimal level it is suggested for the entropy
to be slightly above the average between high and low entropy, this way news
will be easily understandable by the vast majority of people and at the same
time, alluring to the peoples.

  * •

show methodology to predict interaction/visualization from social network to
newspaper.

Social media can perpetuate misinformation about many themes, for example, on
scientific questions such as antimicrobial resistance, and may contribute
indirectly to the misuse of antibiotics R17 . Thus, it is critical that when a
message is delivered, there should not be miscommunication between the sender
and the receiver. To avoid misunderstanding, it must be sure that the message
is clear and straightforward, and counting Questions marks on the comments
about that topic could be a valuable method to rapidly identify doubts.

The clearer and more understandable the message, the lower the observed
entropy level (LEE). The lower the ability to spread clear and comprehensible
messages, the higher the entropy (HEE) level, thus expanding social media’s
potential role in conveying misinformation and hampering the online
newspaper’s work to deliver news and information.

Social media nowadays represent valuable, informative aid for the research. By
adding an entropy analysis to the classification of social events, it was
possible to analyze the miscommunication process by senders, showing the level
of diffusion of information. We observed that if people talk a lot about some
specific topic for many days, they unconsciously need more explication and
information. The case study showed how high entropy events (HEE) and low
entropy events (LEE) are related to specific behavior on social media.
Usually, the ”natural decay” of LEE (predictive and routines events like
football matches) is lower than five hours, showing standard behavior and a
balance right information assimilation.

Conversely, HEE are in general unpredictable: they trigger a need for
additional information, leading to complex behavior that can be observed on
social media. HEE seems to provides an exciting research challenge.

###  6.1 Author Contributions

Investigation, Data resources A.R.; Data elaboration, A.R., A.P. and V.M.;
Methodology, F.M.; Data cleaning and Software, A.R. and G.G.; Correction F.M.
and G.G.
All authors have read and agreed to the published version of the manuscript.

###  6.2 Copyright

Copyright © 2021 SAGE Publications Ltd, 1 Oliver’s Yard, 55 City Road, London,
EC1Y 1SP, UK. All rights reserved.

###  6.3 Funding

The author(s) disclosed receipt of the following financial support for the
research, authorship, and/ or publication of this article: This project has
received funding from the University of Catania.

###  6.4 Author biographies

Andrea Russo is a Ph.D. candidate in Complex Systems at the University of
Catania. He is currently working at the Department of Physics and Astronomy.
His main research field and interests are focused on the study and the
development of Computational social method to explain and research social
complexity, particular field like Politics, Economics, Business and Defense-
Security sector applications.

Antonio Picone is a Ph.D. candidate in Complex Systems at the University of
Catania. His main project and his interests are bound to the field of Natural
Language Processing, with a particular interest in researching ways to
identify sentiments and emotions in a written text with the help of Artificial
Intelligence.

Vincenzo Miracula is a PhD candidate in Complex Systems at the University of
Catania. He currently works at the Department of Physics and Astronomy. His
research interests are in computational social sciences, artificial
intelligence and natural language processing, with a particular interest in
network theory, text analysis and spreading fake news.

Francesco Mazzeo Rinaldi is an Associate Professor at the University of
Catania, where he teaches evaluation research methodology. He is also an
Affiliate Professor at the KTH, Royal Institute of Technology, School of
Architecture and the Built Environment, Stockholm. In the last 15 years, he
carried out training, research, and consulting activities on program and
policy evaluation, monitoring systems, and big data analytics in several
regional, national and international public organizations in social,
development, and cohesion policy.

Giovanni Giuffrida is researcher at the university of Catania. He obtains his
Ph.D at UCLA (Los Angeles) in computer science. CEO of Neodata group SRL,
Founder and CTO of Data Corporation, Santa Monica, California.

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