<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing with OASIS Tables v3.0 20080202//EN" "journalpub-oasis3.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:oasis="http://docs.oasis-open.org/ns/oasis-exchange/table" xml:lang="en" dtd-version="3.0"><?xmltex \bartext{Applied Meteorology and Climatology Proceedings 2020: contributions in the pandemic year}?>
  <front>
    <journal-meta><journal-id journal-id-type="publisher">ASR</journal-id><journal-title-group>
    <journal-title>Advances in Science and Research</journal-title>
    <abbrev-journal-title abbrev-type="publisher">ASR</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Adv. Sci. Res.</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1992-0636</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/asr-18-1-2021</article-id><title-group><article-title>How weather influenced the mood of people during the COVID-19 lockdown in Catalonia: a review <?xmltex \hack{\break}?>of Twitter posts</article-title><alt-title>How weather influenced the mood of people during the COVID-19 lockdown in
Catalonia</alt-title>
      </title-group><?xmltex \runningtitle{How weather influenced the mood of people during the COVID-19 lockdown in
Catalonia}?><?xmltex \runningauthor{T. Molina et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Molina</surname><given-names>Tomàs</given-names></name>
          <email>tomasmolinabosch@ub.edu</email>
        <ext-link>https://orcid.org/0000-0001-8127-6401</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Sancliment</surname><given-names>Alex</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-3398-382X</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Janué</surname><given-names>Jofre</given-names></name>
          
        </contrib>
        <aff id="aff1"><label>1</label><institution>Department of Applied Physics, University of Barcelona, Barcelona,
Spain</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>Televisió de Catalunya, Barcelona, Spain</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Tomàs Molina (tomasmolinabosch@ub.edu)</corresp></author-notes><pub-date><day>29</day><month>January</month><year>2021</year></pub-date>
      
      <volume>18</volume>
      <fpage>1</fpage><lpage>5</lpage>
      <history>
        <date date-type="received"><day>14</day><month>November</month><year>2020</year></date>
           <date date-type="rev-recd"><day>5</day><month>January</month><year>2021</year></date>
           <date date-type="accepted"><day>6</day><month>January</month><year>2021</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2021 Tomàs Molina et al.</copyright-statement>
        <copyright-year>2021</copyright-year>
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://asr.copernicus.org/articles/18/1/2021/asr-18-1-2021.html">This article is available from https://asr.copernicus.org/articles/18/1/2021/asr-18-1-2021.html</self-uri><self-uri xlink:href="https://asr.copernicus.org/articles/18/1/2021/asr-18-1-2021.pdf">The full text article is available as a PDF file from https://asr.copernicus.org/articles/18/1/2021/asr-18-1-2021.pdf</self-uri>
      <abstract><title>Abstract</title>
    <p id="d1e102">This article is the result of a campaign done during the
COVID-19 lockdown in Catalonia. The Television of Catalonia audience was
involved in an action to inform about the weather from their own homes by
posting Twitter videos. Some of the videos were shown on air in the weather
segment of the television station's main news programs. We have correlated
participation in the campaign with meteorological and public health data and
found that weather is related to the mood of people when using social media platforms such as Twitter.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d1e114">The pandemic caused by the spread of the new coronavirus SARS-CoV-2 had a
strong incidence in Catalonia, an autonomous region in the NE of the Iberian
peninsula (Fig. 1). Igualada, one of the biggest cities in central
Catalonia, was one of the first
areas to be placed under lockdown in Europe, days before the total
lockdown of the entire country imposed by the Spanish authorities from 15 March 2020. The Spanish lockdown was one of the strictest in Europe and
consisted in the closure of all non-essential activities, the suspension of
academic activity and the reduction of public transport and other public
services. This lockdown was even more restrictive during the two weeks
starting on 31 March, when the Spanish authorities suspended nearly all the
remaining activities following a surge in COVID-19 incidence.</p>
      <p id="d1e117">After the first case of the virus was identified in Catalonia on 25 February and until June, more than 58 000 people were diagnosed with the virus,
and 12 408 of them died. The most acute phase of the pandemic in the region
was from 26 March and 12 April, with over 300 daily deaths, reaching a
record-high on 30 March with 414 victims. 30 April was the first day with
less than 100 deaths. The combined effect of health and lockdown measures
with the rise of temperatures (Tobias and Molina, 2020) at the beginning of
May led to a decrease in the number of deceased, getting below 50 a day on
10 May. Starting on the first Saturday of May (2 May), citizens were allowed again to walk in the
street by groups of ages in the first of several phases of the de-escalation
from the lockdown.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><?xmltex \currentcnt{1}?><?xmltex \def\figurename{Figure}?><label>Figure 1</label><caption><p id="d1e122">Counties of Catalonia and location of the region in the
context of Spain.</p></caption>
        <?xmltex \igopts{width=369.885827pt}?><graphic xlink:href="https://asr.copernicus.org/articles/18/1/2021/asr-18-1-2021-f01.png"/>

      </fig>

      <p id="d1e132">During the lockdown the Catalan public TV (Televisió de Catalunya, TV3),
a TV channel with two main news programs every day at noon and in the
evenings, reached its highest audiences in the last decade. The weather
segment of 5 min at the end of each news programme had more than
400 000 viewers at its noon edition and 700 000 viewers for its evening
edition, according to Kantar media (2021). On 18 March, viewers were encouraged
to send short videos explaining the current weather conditions at home using
the Twitter hashtag “ElTempsdesdecasaTV3”, which translates to “Weather
from home TV3”. Some of the videos were shown on air in the weather segment
of the main news. The campaign was a big success and thousands of viewers,
most of them non-professional weather observers, sent their videos from over
half of the country's towns and cities (Fig. 2).</p>
      <p id="d1e135">It is well known that weather influences people's behaviour and activity,
and plays a role in a person's mood  (Golder and Macy, 2011; Park et
al., 2013). Temperature and humidity have an influence in depressive moods
(Modoni and Tosi, 2016), with people living under higher temperatures being
less predisposed to depression. During the pandemic lockdown people were
forced to stay at home. Studies have<?pagebreak page2?> shown that Spanish users used social
media to inform themselves and to buy products like nuts, cheese and
chocolate, or desserts, to improve their mood during this period (Laguna et
al., 2020). It was a difficult time for most. Emergency events, and those related to mass coverage, are some
of those most suitable for the use and sharing of information on Twitter
(Hughes and Palen, 2009). The total lockdown, with millions of people
staying at home, with anger in their present and future health security, can
be a good case study.</p>
</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Data</title>
      <p id="d1e146">The Twitter campaign “ElTempsdesdecasaTV3” lasted from 18 March until 10 May. The viewers of El Temps, the weather segment of TV3, were asked to post short videos on Twitter about the
current weather conditions as seen from their own home windows and terraces
(following the rules of the lockdown imposed by Spanish authorities). The
video was accompanied with a speech about the place, time and temperature of
the location where the video had been taken. During this period a total of
5787 videos were posted on the social media platform Twitter. We conducted
a study of the number of posts per day, the owner of each Twitter account,
and the location of each video.</p>
      <p id="d1e149">We collected daily data of cases confirmed from a positive polymerase
chain reaction (PCR) test and mortality between 18 March and 10 May 2020.
Data was provided by the Health Evaluation and Quality Agency of Catalonia
(AQuAS). Daily average, minimum and maximum ambient temperature, absolute
humidity and solar radiation levels were provided by the Meteorological
Service of Catalonia (MeteoCat).</p>
      <p id="d1e152"><?xmltex \hack{\newpage}?>We estimated the average meteorological factors in Catalonia as the average
for Barcelona, Tarragona, Lleida and Girona, the four capital cities of the Catalan provinces; Igualada, the most
affected city during the first weeks of the pandemic and located near the
centre of Catalonia; and Tortosa and La Seu d'Urgell, two towns
representative of the south and the Pyrenees regions.</p>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Results</title>
      <p id="d1e164">From 18 March to the end of the month there were 1244 videos mentioning the
hashtag “ElTempsdesdecasaTV3” on Twitter, coming from 382 different locations. April had 3852 participations from
626 different locations. The month of May, until 10 May, had 691 videos from
247 locations.</p>
      <p id="d1e167">With a total of 5787 videos with the hashtag, the average was 107 videos a
day, with a maximum of 208 participations on 19 April. The 10 cities with
more participation, which include Barcelona and other highly-populated
areas, represent nearly a quarter of the posts (Table 1).</p>

<?xmltex \floatpos{t}?><?pagebreak page3?><table-wrap id="Ch1.T1"><?xmltex \currentcnt{1}?><?xmltex \def\figurename{Table}?><label>Table 1</label><caption><p id="d1e173">Locations with a higher share of the total number of
tweets.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="3">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="left"/>
     <oasis:colspec colnum="3" colname="col3" align="left"/>
     <oasis:thead>
       <oasis:row>
         <oasis:entry colname="col1">Location</oasis:entry>
         <oasis:entry colname="col2">Tweets</oasis:entry>
         <oasis:entry colname="col3">Percentage of</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3">total tweets</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">Barcelona</oasis:entry>
         <oasis:entry colname="col2">463</oasis:entry>
         <oasis:entry colname="col3">8.0 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Gavà</oasis:entry>
         <oasis:entry colname="col2">151</oasis:entry>
         <oasis:entry colname="col3">2.6 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Lleida</oasis:entry>
         <oasis:entry colname="col2">113</oasis:entry>
         <oasis:entry colname="col3">2.0 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Manresa</oasis:entry>
         <oasis:entry colname="col2">107</oasis:entry>
         <oasis:entry colname="col3">1.9 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Vic</oasis:entry>
         <oasis:entry colname="col2">82</oasis:entry>
         <oasis:entry colname="col3">1.4 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">La Llacuna</oasis:entry>
         <oasis:entry colname="col2">81</oasis:entry>
         <oasis:entry colname="col3">1.4 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Mataró</oasis:entry>
         <oasis:entry colname="col2">73</oasis:entry>
         <oasis:entry colname="col3">1.3 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Porqueres</oasis:entry>
         <oasis:entry colname="col2">69</oasis:entry>
         <oasis:entry colname="col3">1.2 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Blanes</oasis:entry>
         <oasis:entry colname="col2">63</oasis:entry>
         <oasis:entry colname="col3">1.1 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Tarragona</oasis:entry>
         <oasis:entry colname="col2">62</oasis:entry>
         <oasis:entry colname="col3">1.0 %</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

      <p id="d1e337">Most of the participants made one or two contributions to the campaign,
though three participants were responsible for 267 videos and the 10 most
active participants represent a tenth of the total number of posts (Table 2).</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T2"><?xmltex \currentcnt{2}?><?xmltex \def\figurename{Table}?><label>Table 2</label><caption><p id="d1e343">Users with a higher share of the total number of tweets.</p></caption><oasis:table frame="topbot"><?xmltex \begin{scaleboxenv}{.98}[.98]?><oasis:tgroup cols="3">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="left"/>
     <oasis:colspec colnum="3" colname="col3" align="left"/>
     <oasis:thead>
       <oasis:row>
         <oasis:entry colname="col1">Location</oasis:entry>
         <oasis:entry colname="col2">Tweets</oasis:entry>
         <oasis:entry colname="col3">Percentage of</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3">total tweets</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">Single user from Gavà</oasis:entry>
         <oasis:entry colname="col2">139</oasis:entry>
         <oasis:entry colname="col3">2.4 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Single user from Porqueres</oasis:entry>
         <oasis:entry colname="col2">69</oasis:entry>
         <oasis:entry colname="col3">1.2 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Single user from La Llacuna</oasis:entry>
         <oasis:entry colname="col2">59</oasis:entry>
         <oasis:entry colname="col3">1.0 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Single user from Claret</oasis:entry>
         <oasis:entry colname="col2">50</oasis:entry>
         <oasis:entry colname="col3">0.9 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Single user from Tossa de Mar</oasis:entry>
         <oasis:entry colname="col2">46</oasis:entry>
         <oasis:entry colname="col3">0.8 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Single user from Barcelona</oasis:entry>
         <oasis:entry colname="col2">45</oasis:entry>
         <oasis:entry colname="col3">0.8 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Single user from Blanes</oasis:entry>
         <oasis:entry colname="col2">44</oasis:entry>
         <oasis:entry colname="col3">0.8 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Single user from Parets del Vallès</oasis:entry>
         <oasis:entry colname="col2">43</oasis:entry>
         <oasis:entry colname="col3">0.7 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Single user from Manresa</oasis:entry>
         <oasis:entry colname="col2">42</oasis:entry>
         <oasis:entry colname="col3">0.7 %</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Single user from Masquefa</oasis:entry>
         <oasis:entry colname="col2">41</oasis:entry>
         <oasis:entry colname="col3">0.7 %</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup><?xmltex \end{scaleboxenv}?></oasis:table></table-wrap>

      <p id="d1e506">More than half of the municipalities of Catalonia sent at least one tweet
for the campaign, with tweets coming from 778 different locations. Figure 2
shows the geographical participation distribution along the Twitter
campaign. Most populated areas had more participants, while rural areas had
less, even none.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2"><?xmltex \currentcnt{2}?><?xmltex \def\figurename{Figure}?><label>Figure 2</label><caption><p id="d1e511">Spatial distribution of the tweets.</p></caption>
        <?xmltex \igopts{width=241.848425pt}?><graphic xlink:href="https://asr.copernicus.org/articles/18/1/2021/asr-18-1-2021-f02.png"/>

      </fig>

<?xmltex \hack{\newpage}?>
</sec>
<sec id="Ch1.S4">
  <label>4</label><title>Discussion</title>
      <p id="d1e530">Figure 3 shows participation, number of cases per day, and daily deaths in
Catalonia. The number of tweets increased until mid-April, and after that
diminished as the number of cases and daily deaths decreased until the end
of the lockdown period.</p>

      <?xmltex \floatpos{p}?><?pagebreak page4?><fig id="Ch1.F3" specific-use="star"><?xmltex \currentcnt{3}?><?xmltex \def\figurename{Figure}?><label>Figure 3</label><caption><p id="d1e535">Confirmed COVID-19 cases and deaths and tweets by
viewers.</p></caption>
        <?xmltex \igopts{width=369.885827pt}?><graphic xlink:href="https://asr.copernicus.org/articles/18/1/2021/asr-18-1-2021-f03.png"/>

      </fig>

      <p id="d1e544">Figure 4 shows participation, mean temperature and mean solar radiation.
Participation in the campaign tends to diminish with the increase in
temperature and in the amount of solar radiation. It is notable that the
days with less solar radiation are those with a significant increase in participation. The maximum
participation day, 19 April, with 208 tweets, was a day of extensive and
heavy precipitation across Catalonia. The five days with more participation
were also correlated with rainy days, while the 5 d with less
participation were days with no significant weather nor clouds.</p>

      <?xmltex \floatpos{p}?><fig id="Ch1.F4" specific-use="star"><?xmltex \currentcnt{4}?><?xmltex \def\figurename{Figure}?><label>Figure 4</label><caption><p id="d1e550">Solar radiation, temperature and tweets by viewers.</p></caption>
        <?xmltex \igopts{width=369.885827pt}?><graphic xlink:href="https://asr.copernicus.org/articles/18/1/2021/asr-18-1-2021-f04.png"/>

      </fig>

      <p id="d1e559">In a visual comparison with the ratio of incidence cases per 100 000
population, and county mean temperature (Fig. 5), we only find that most
of the participants came from areas with more population and in this case
with more COVID-19 incidence,  and also from coastal areas with warmer temperatures.</p>

      <?xmltex \floatpos{p}?><fig id="Ch1.F5" specific-use="star"><?xmltex \currentcnt{5}?><?xmltex \def\figurename{Figure}?><label>Figure 5</label><caption><p id="d1e564"><bold>(a)</bold> COVID-19 incidence (cases per 100 000 pop.); <bold>(b)</bold> Mean
temperature (<inline-formula><mml:math id="M1" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C) (Tobias et al., 2021).</p></caption>
        <?xmltex \igopts{width=398.338583pt}?><graphic xlink:href="https://asr.copernicus.org/articles/18/1/2021/asr-18-1-2021-f05.png"/>

      </fig>

<?xmltex \hack{\newpage}?>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <label>5</label><title>Conclusions</title>
      <p id="d1e598">The Covid-19 pandemic affected the mood of people in Catalonia in many ways:
the anger of the increasing number of infected persons, the number of daily
deaths, the sadness feelings for those who lost beloved ones, and the
loneliness and bewilderment of many families. The lockdown period forced an
entire society to stay at home, with very few distractions or social
interaction.</p>
      <p id="d1e601">Social media and TV programs, especially news segments, were – to a large
extent – the main source of communication and information from the outside
for many. The campaign ElTempsdesdecasaTV3 was a welcomed proposal by people
at home, to follow what was happening in other places of the country, giving them something to do and to talk about while in lockdown.</p>
      <p id="d1e604">Participation in the campaign had an ascending curve from the beginning of
March until mid-April with winter temperatures and few hours of sun. From
the second half of the month of April, when the mean temperature was over
15 <inline-formula><mml:math id="M2" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C and with the increase in sunlight, the participation tendency changed and suddenly
decreased.</p>
      <p id="d1e616">Rainy days are sparse in Catalonia, around 80 d a year or less in most
cities. The days with precipitation, those with sudden decrease in solar
radiation (Fig. 4), are the days with more tweets with videos of the
campaign. This shows that weather conditions had an immediate effect on
people's moods and attitudes, leading to an increase in social media posts
related to the campaign.</p>
      <?pagebreak page5?><p id="d1e620">After 2 May people were allowed to go out from their homes, and
participation in the campaign decreased because the mood changed, leading to
the suspension of the campaign. The population did not need any more of the
campaign, so it was cancelled.</p>
</sec>

      
      </body>
    <back><notes notes-type="dataavailability"><title>Data availability</title>

      <p id="d1e627">Online dataset published by the Health Evaluation
and Quality Agency of Catalonia (AQuAS) for the COVID-19 data.
20 Online dataset published by Catalonia's
Meteorological Service (MeteoCat) for the weather data.</p>
  </notes><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e633">TM and AS conceived the idea of this paper. AS extracted the Twitter data and JJ extracted the
COVID19 and meteorological data and performed the graphs. TM
and AS wrote the manuscript.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e639">The authors declare that they have no conflict of interest.</p>
  </notes><notes notes-type="sistatement"><title>Special issue statement</title>

      <p id="d1e645">This article is part of the special issue “Applied Meteorology and Climatology Proceedings 2020: contributions in the pandemic year”.</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e651">This paper was edited by Tanja Cegnar and reviewed by Haleh Kootval and one anonymous referee.</p>
  </notes><ref-list>
    <title>References</title>

      <ref id="bib1.bib1"><label>1</label><?label 1?><mixed-citation>Golder, S. A. and Macy, M. W.: Diurnal and Seasonal Mood Vary with Work,
Sleep, and Daylength Across Diverse Cultures, Science, 1878–1881,
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    <!--<article-title-html>How weather influenced the mood of people during the COVID-19 lockdown in Catalonia: a review of Twitter posts</article-title-html>
<abstract-html><p>This article is the result of a campaign done during the
COVID-19 lockdown in Catalonia. The Television of Catalonia audience was
involved in an action to inform about the weather from their own homes by
posting Twitter videos. Some of the videos were shown on air in the weather
segment of the television station's main news programs. We have correlated
participation in the campaign with meteorological and public health data and
found that weather is related to the mood of people when using social media platforms such as Twitter.</p></abstract-html>
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</mixed-citation></ref-html>--></article>
