ASRAdvances in Science and ResearchASRAdv. Sci. Res.1992-0636Copernicus PublicationsGöttingen, Germany10.5194/asr-14-217-2017Public crowdsensing of heat waves by social media dataGrassoValentinav.grasso@ibimet.cnr.ithttps://orcid.org/0000-0002-1433-1674CrisciAlfonsoMorabitoMarcoNesiPaoloPantaleoGianniInstitute of Biometeorology, Italian National Research Council, Via G. Caproni 8, Florence, ItalyLaMMA Consortium, Via Madonna del Piano 10, Sesto Fiorentino (FI), ItalyDISIT Lab, Distributed Systems and internet/Data Intelligence and Technologies Lab,
Dep. of Information Engineering (DINFO), University of Florence, Italy Via S. Marta, Florence, ItalyValentina Grasso (v.grasso@ibimet.cnr.it)11July20171421722624January20174May20177June2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://asr.copernicus.org/articles/14/217/2017/asr-14-217-2017.htmlThe full text article is available as a PDF file from https://asr.copernicus.org/articles/14/217/2017/asr-14-217-2017.pdf
Investigating on society-related heat wave hazards is a global issue
concerning the people health. In the last two decades, Europe experienced
several severe heat wave episodes with catastrophic effects in term of human
mortality (2003, 2010 and 2015). Recent climate investigations confirm that
this threat will represent a key issue for the resiliency of urban
communities in next decades. Several important mitigation actions
(Heat-Health Action Plans) against heat hazards have been already implemented
in some WHO (World Health Organization) European region member states to
encourage preparedness and response to extreme heat events. Nowadays, social
media (SM) offer new opportunities to indirectly measure the impact of heat
waves on society. Using the crowdsensing concept, a micro-blogging platform
like Twitter may be used as a distributed network of mobile sensors that
react to external events by exchanging messages (tweets). This work presents
a preliminary analysis of tweets related to heat waves that occurred in Italy
in summer 2015. Using TwitterVigilance dashboard, developed by the University
of Florence, a sample of tweets related to heat conditions was retrieved,
stored and analyzed for main features. Significant associations between the
daily increase in tweets and extreme temperatures were presented. The daily
volume of Twitter users and messages revealed to be a valuable indicator of
heat wave impact at the local level, in urban areas. Furthermore, with the
help of Generalized Additive Model (GAM), the volume of tweets in certain
locations has been used to estimate thresholds of local discomfort
conditions. These city-specific thresholds are the result of dissimilar
climatic conditions and risk cultures.
Introduction
Use of social media (SM) during emergencies to communicate timely information
has become a practice in the last years. Social media have been used for
disaster detection, risk prevention, communication situational awareness, and
scientific knowledge. Scholars investigated the use of SM, and particularly
Twitter, in different natural disaster situations: earthquakes
, wild fires
, floods
,
hurricanes .
SM enable data collection at an unprecedented scale, allowing to record
public attention and reactions to events unfolding in both virtual and
physical worlds deep involving social science research
. However not all social media data are effectively
available for research purposes due to platform policies which limit the
access to messages. A platform like Twitter, for instance, make available to
researchers and analysts only a sample of the public data stream. Access is
usually provided by mean of APIs (Application Programming Interfaces) but
only to a sampling data set, which for Twitter is around 1 % of the current
public data stream . Furthermore, APIs structure
produces different data retrieval outcomes: Search APIs and Streaming APIs
may produce different sample data sets . Other
limitations in SM use for research purposes are related to queries. By
searching for messages containing selected keywords the retrieved data set
is, in fact, only a sample of the whole Twitter conversations around a topic.
Nevertheless, scholars have been using social media for research purposes on
a variety of domains.
Among the different fields, scholars have begun to use SM to extract
information about disaster events
. On social media we may
find digital traces of a disaster which can be used to derive the strength
and impact of an event. For instance, used this
approach to verify the link between the spatio-temporal distribution of
tweets and the physical extent of floods; derived
Sandy Hurricane per-capita damages by analyzing Twitter activity. Until now
little attention has been reserved to the role of SM during
heat-waves-related crisis . Extreme temperatures are
in fact recognized as critical and this is also confirmed by the recent
report produced by the U.S Global Change Research Program
which reported that heat waves revealed the highest
10-year estimates of fatalities and represented the second (after hurricanes)
estimated economic damages among the main weather and climate disaster events
in United States from 2004 to 2013. Heat waves are a silent killer, mostly
affecting vulnerable people like the elderly and the
very young . Heat waves can be considered a crisis as
they affect the environment, the infrastructure and also the security of
citizens. The impact of extreme high temperatures on mortality is
particularly high in Europe, accounting for over 80 % of the total
heat-wave-related deaths worldwide (source EM-DAT, http://www.emdat.be/).
From previous studies we know that the information shared on Twitter varies
greatly form one crisis to another
. Information contents and
sources are in fact impacted by various dimensions, like the hazard type
(natural hazard or human-induced), the temporal development (instantaneous
– like earthquake – or progressive – like hurricane or heat wave), and the
geographical spread (localized or diffused). For instance, tweets published
from Governments are more diffused in crisis related to natural hazards,
which are progressive and diffused. Those are the case where warning are
issued.
This work presents a contribution on the use of Twitter during heat waves, a
domain not fully explored so far. Most of the studies that investigated the
effects of heat waves on health have used sanitary-type indicators (i.e.
general and case-specific mortality, hospital admissions, emergency room
visits) while the use of SM to evaluate the effect of heat waves on
population is poorly studied. In this work we explore the use of SM data as
an indicator of heat waves impact on urban population. We considered Tweets
as a form of crowdsensing. We refer to crowdsensing as a form of
participatory sensing where citizens voluntary
collaborate to data collection and sharing using their devices. Twitter
mining represents an indirect form of crowdsensing with no explicit
engagement of people into data collection. In this case user-generated
contents, like tweets, are used for a second purpose in what may be seen as a
mobile Crowdsensing . The derived data may enable
better events detection, timely trend and anomaly analysis and a faster
response. The advantage of this approach is connected to the timely
information delivered by SM and the possibility to identify the most
vulnerable areas as “hot-spots” thanks to the geographical insights
obtained by tweets. Heat waves have in fact their maximum impact in urban
environments , characterized by the aggravating
phenomenon of the urban heat island. Furthermore, the majority of the
population is concentrated in cities, like also are the most vulnerable
subjects (i.e. the elderly living alone). In particular, based on the
Eurostat database
(http://ec.europa.eu/eurostat/statistics-explained/index.php/Population_and_social_conditions),
while a higher proportion of the elderly population of the EU-28 countries
lived in rural regions, those who were in urban regions were more likely to
be living alone. The latter is a well-known risk factor for heat-related
mortality .
The study aims to attest the reliability of SM data as a form of crowdsensing
of heat waves impact in urban environment. In particular the following
aspects are investigated: verify the usefulness of SM as social indicators of
thermal impacts on the population; perform a data-driven estimation of
city-specific thresholds of apparent temperature associated with a peak in
volumes of tweets, that may be used as a quantitative risk assessment for
each location.
Daily volumes of tweets collected by TwitterVigilance in the channel
“Heat” (caldo) during May–September 2015.
Research design and methodology
Compared to many biometeorological studies, this work proposes an innovative
way to investigate and assess the impact of heat wave episodes by using
social media data. The analysis considered heat waves occurred in Italy
during the very hot summer of 2015. In particular, according to data of the
Institute of Atmospheric Sciences and Climate of the National Research
Council
(https://www.cnr.it/en/news/6284/estate-2015-la-terza-per-temperature-dal-1800),
the summer of 2015 was for Italy the third hottest summer since 1800 (after
the 2003 and the 2012 summers). Furthermore, the positive temperature anomaly
observed during July 2015 was the highest ever observed in Italy. Twitter
data in reason of its publicity may be a real-time informative source to
monitor impacts of extreme temperatures during heat waves. The methodological
approach is based on the exploitation of crowdsensed data semantically linked
to heat perception. The main research questions of this study may be
summarized as follow:
Are heat waves actually associated to social media streams semantically related to “heat”?
Does the social media activity “follow” the spatial and temporal pattern of heat waves?
Is a daily climatic classification of heat wave (i.e. heat wave days) able to discriminate different levels social media activity?
Is it possible to use social media to identify local thresholds of heat discomfort?
We used the heat wave definition as provided by EuroHEAT Project (Improving
Public Health Responses to extreme weather/heat waves). Following D'Ippoliti
et al. (), heat wave is defined as a period
equal to or longer than two Heat Critical day. This one is defined as a day
with maximum apparent temperature exceeding the 90th percentile of the
monthly distribution or the ones which minimum temperature exceeds the 90th
percentile and maximum apparent temperature exceeds the median monthly
climatic referenced value. In our study, the climatic monthly references were
assessed for any location considered by using a 30-days filtered daily
normals by using a running mean along the year span. Heat waves occurring in
Italy during the summer of 2015 (from 15 May to 15 September ) were assessed
by using meteorological data obtained by the NOAA GSOD – Global Surface
Summary of the Day
(https://data.noaa.gov/dataset/global-surface-summary-of-the-day-gsod)
for 21 locations corresponding to the most important Italian cities (see Appendix
for location list). Daily maximum apparent temperature was assessed by using
the Steadman approach and critical and heat
wave days were calculated for the period 15 May–15 September 2015
(N=124). The regions of the North Tyrrhenian were the most impacted in July.
On the other hand, the August's episode involved the southern eastern areas
(mainly Apulia and Calabria).
Number of Tweets and Retweets and the maximum apparent temperature
(average of all stations) for Italy during the period 15 May and 15 September
of 2015.
For the same time interval, tweets filtered for semantically relevant
criteria were collected and stored, with the aim to compare the volume of
messages and the spatial and temporal pattern of heat waves. Twitter
retrieval and storage was performed with the help of the TwitterVigilance, a
web-platform developed by the DISIT Lab of the University of Florence.
(http://www.disit.org/6693). TwitterVigilance is a multi-user tool for
Twitter analysis that allows to create and manage multiple parameters for
Twitter API querying, to store the tweets data into channels, defined by
users. The TwitterVigilance is also a dashboard for fast visualization of
main analytics of each channel, as shown in Fig. 1.
To monitor Twitter activity related to extreme temperature conditions we
created a “Heat” monitoring channel on TwitterVigilance platform based on a
set of keywords and hashtags semantically related to heat conditions. From
the total retrieved messages, the original tweets were filtered following
most occurring words in Italian language like: caldo (hot), afa (very hot),
canicola, sudo, sudato, sudore (sweat), caldissimo (very hot), torrido
(scorching), record, allarme (alarm), emergenza (emergency), bollino (mark),
bere (drink), anziani (senior), sete (thirsty, dry), umidità (umidity),
anticiclone (anticyclon), disagio (discomfort), umido (umid, weat), Caronte/Flegetonte (by media naming of high-pressure systems). By choosing Italian
language for queries, we excluded tweets in foreigns languages published by
tourists or immigrants living in the country, but in this first study we
preferred to concentrate on reactions coming from native Italian citizens. To
reasonably correlate daily volumes of tweets with daily temperatures we had
to rely on tweets containing a geographic reference. Location estimation is
one of the main problem to approach working with Twitter data set. Even if it
is possible on Twitter to communicate geo-location information by enabling
Global Positioning System (GPS) when using the App, in practice only few
messages include this information. Scholars indicate that the percentage of
tweets that has geo-location meta-data is about 2 % .
When geo-tagging is not provided, it is possible to derive the location of a
user by examining his profile description or tweets in his stream; otherwise
it is possible to infer location by examining tweet content with specific
algorithms based on entity recognition and Natural Language Processing. We
inferred location by using this last technique. “Heat” related tweets were
partitioned into city/regional streams through the geographical key-terms
linked to the locations considered; both the “city name” and “region
name” (see Appendix for full locations list). For each local SM data subset,
main Twitter metrics were computed as to be compared with relative thermal
data. Metrics used were: daily number of tweets (which includes both native
tweets and retweets), daily number of native tweets, daily number of
retweets, daily number of unique users. Data are summarized in Table 1. The
significance of social media metrics vs temperatures association
(T∘ and Apparent T∘ max) was tested by using a linear
correlation scheme. To test if SM metrics are significantly different during
heat waves a t-Student test (t-test) was performed by using daily heat wave
status as stratified factor. The data set of retrieved tweets has to be
considered as a sample of the whole Twitter conversations, in Italian,
mentioning heat during the monitored period.
∗ Areas are the results of messages containing the name
of the local cities considered and/or the name of the region they belong to
(see Appendix for full location list). Ntw = Native tweets;
RT = Retweets.
The pattern of the daily number of tweets (green histogram) and
retweets (blue histogram) and the daily maximum apparent temperatures (red
line) and the heat-waves periods (transparent blue bars) for Milan.
Results and discussion
From 15 May to 15 September 2015 through the TwitterVigilance channel and by
applying semantic filters explained above, we collected 940 123 tweets sent by
233 553 unique users. The data set is composed of 585 286 native tweets (62 %
of the whole data set), tweets originally published by users, and 354 837 retweets (38 %), tweets written by other users that are reposted, typically
starting with RT: @username. Table 1 shows main metrics for the
whole data set and for main areas considered. The areas with greater number
of tweets and users are those more impacted by heat wave events. Some regions
impacted by heat wave episodes do not show however similar volumes of tweets,
reasonably because they are less populated rural areas where Twitter users
are few and few are the tweets mentioning heat related contents.
Daily Twitter data were compared to weather data and a clear association was
observed, at national level, between the number of Tweets and Retweets and
the maximum apparent temperatures (the average of all stations), as it is
shown in Fig. 2.
In particular, five main heat waves episodes were detected in Italy during
the summer of 2015. The statistical association among daily volumes of tweets
and apparent temperatures (and the difference of the ones during heat wave
days) is stronger in cities with high population density, like Milan, than in
other cities. As an example Fig. 3 shows the graph of the patterns of the
daily number of tweets and retweets related to heat (green and blued bars),
the daily maximum apparent temperatures (red line) and the heat wave
occurrences in Milan. In this case the association among tweets volumes and
heat temperature is highly statistically significant.
Social heat reliability in several Italian cities. Read spot:
significant association; blue spot: not significant association.
GAM Predicted daily Tweets volumes by Maximum Apparent Temperatures
as a function of the x axis in Milan (b) and Rome (a).
Values are centered on 0.
Except for Northeast, Piedmont, Apennines areas and Calabria, the great part
of locations and regions revealed significant associations between Twitter
metrics and the increase in apparent temperatures (Fig. 4). Red spot in the
map represents locations where the association among tweets daily volumes and
maximum temperatures is significant, blue spot are locations with no
significant association. Association is reliable in locations where tweets
are sufficiently numerous. In more rural regions with lower population
density Twitter users are fewer. The small volume of tweets related to these
locations is not suitable for a significant association.
Other interesting results were obtained by using Generalized Additive Models
(GAM) analyses in different cities. GAMs are a
non-linear extension of generalized linear models that perform well in this
kind of predictive analysis. By modeling the daily volume of tweets
mentioning heat, it is possible to estimate city-specific thresholds of local
maximum apparent temperature. These thresholds correspond to breakpoints in
the range, where the number of tweets suddenly increases. In southern cities
the growth of heat-related twitter activity shows higher thermal threshold
(37 ∘C in Rome) than in northern ones (33 ∘C in Milan)
(Fig. 5). These thresholds correspond to the levels of maximum apparent
temperature where people begin to perceive heat discomfort. City-specific
thresholds imply a local heat risk perception that could be linked to urban
climatic factors of the city investigated. These thresholds are also a
product of local risk cultures and are, in fact, higher in locations with
warmer climate or closer to the sea, like Rome, respect to Milan (see Fig. 5). In this respect, tweets prove to be a valuable form of crowdsensing to
detect heat waves impact in urban areas. When Apparent Maximum Temperature
reaches a city-specific threshold, people start to comment or complain about
the heat. Knowing local thresholds for main urban areas may be important to
improve preparedness measures at the regional and local level and reduce
local vulnerability. Lexical analysis of tweets contents could potentially
offer further understanding about specific impacts, local discomforts or
health symptoms.
Conclusions
The analysis of summer 2015 heat wave episodes through social media analytics
showed that sudden growth of Twitter activity related to heat conditions
seems to identify correctly the peak days of heat wave episodes and also
allows a geographical identification of high impact situations. During heat
related crisis this may facilitate response efforts at local level,
especially if more geolocated tweets are available. Following a crowdsensing
approach, daily volumes of tweets related to heat may thus be considered as a
further indicator to assess heat waves impact at national level and even
local. Compared to the most used sanitary-type indicators (i.e. general and
case-specific mortality, hospital admissions, emergency room visits), Twitter
volumes may be obtained faster and easier, with the help of data retrieval
and storage platforms like TwitterVigilance. In this first contribution,
authors did not considered the nature and contents of tweets, which could
instead provide further information and feedbacks about local perceptions and
impacts of heat waves. This could be the subject of a following research
work.
Responsive tools to monitor the impacts of heat waves are few. Social media
analytic shows a twofold usefulness for emergency/disaster management.
Firstly, SM activity metrics give a quantitative and reliable feedback from
large urban areas where the heat risk is higher and where vulnerable people
generally live. Secondly, SM monitoring may also provide at the same time an
alternative communication channel to reach urban population and increase
situational awareness on heat related risks.
The code of the work is available at the public repository
https://github.com/alfcrisci/EMS_heat_twitter/tree/master/code (Crisci et al., 2016a).
All data used in the work are available in RDS file format
https://github.com/alfcrisci/EMS_heat_twitter/tree/master/data (Crisci et al., 2016b).
Location filtering
Regions name used for filtering: Marche, Puglia, Emilia Romagna, Trentino
Alto Adige, Sardegna, Molise, Calabria, Toscana, Liguria, Lombardia, Sicilia,
Umbria, Abruzzo, Lazio Piemonte, Friuli Venezia Giulia, Veneto.
Locations name used for filtering: Ancona, Bari, Bologna, Bolzano, Cagliari,
Campobasso, Catania, Crotone, Firenze, Genova, Lecce, Milano, Napoli,
Palermo, Perugia, Pescara, Reggio Calabria, Roma, Torino, Trieste, Venezia.
The authors have declared that no competing interests
exist.
The article reflects only the authors' view and the European
Commission is not responsible for any use that may be made of the information
it contains.
This article is part of the special issue “16th EMS Annual
Meeting & 11th European Conference on Applied Climatology (ECAC)”. It is
a result of the 16th EMS Annual Meeting & 11th European Conference on
Applied Climatology (ECAC), Trieste, Italy, 12–16 September 2016.
Acknowledgements
The article represents the results of the CARISMAND project which has
received funding from the European Union's Horizon 2020 research and
innovation programme under grant agreement No. 653748.
Edited by: Antti Makela
Reviewed by: Laure Fallou, Hayley Watson, and one anonymous referee
References
Bossu, R., Mazet-Roux, G., Roussel, F., Steed, R., and Etivant, C.: The EMSC
tools used to detect and diagnose the impact of global earthquakes from
direct and indirect eyewitnesses' contributions, in: Information Systems
for Crisis Response and Management (ISCRAM) 2015 Conference Proceedings, 24–27 May 2015,
Kristiansand, Norway, 24–27, 2015.
Boyd, D. and Crawford, K.: Critical questions for big data: Provocations for
a cultural, technological, and scholarly phenomenon, Information,
Communication & Society, 15, 662–679, 2012.
Bruns, A. and Burgess, J.: Crisis communication in natural disasters: The
Queensland floods and Christchurch earthquakes, Twitter and society, 89,
373–384, 2014.
Burke, J., Estrin, D., Hansen, M., Parker, A., Ramanthan, N., Reddy, S., and
Srivastava, M. B.: Participatory Sensing, in: World Sensor Web Workshop, ACM
Sensys, ACM, 31 October–3 November, Boulder, Colorado, USA, 2006.Burton, S. H., Tanner, K. W., Giraud-Carrier, C. G., West, J. H., and Barnes,
M. D.: “Right time, right place” health communication on Twitter: value and
accuracy of location information, J. Med. Internet Res., 14,
e156, 10.2196/jmir.2121, 2012.Crisci, A., Grasso, V., Morabito, M., Nesi, P., and Pantaleo, G.:
EMS_heat_twitter/code/, Repository of “Public crowdsensing of heat waves
by social media data” – EMS conference contribution, CNR – IBIMET,
available at:
https://github.com/alfcrisci/EMS_heat_twitter/tree/master/code (last
access: 2 January 2017), 2016a.Crisci, A., Grasso, V., Morabito, M., Nesi, P., and Pantaleo, G.:
EMS_heat_twitter/data/, Repository of “Public crowdsensing of heat waves
by social media data” – EMS conference contribution, CNR – IBIMET,
available at:
https://github.com/alfcrisci/EMS_heat_twitter/tree/master/data (last
access: 2 January 2017), 2016b.D'Ippoliti, D., Michelozzi, P., Marino, C., de'Donato, F., Menne, B.,
Katsouyanni, K., Kirchmayer, U., Analitis, A., Medina-Ramón, M., Paldy,
A., Atkinson, R., Kovats, S., Bisanti, L., Schneider, A., Lefranc, A., Iñiguez, C., and Perucci, C. A.: The impact of heat waves on mortality in 9 European cities:
results from the EuroHEAT project, Environ. Health, 9, 10.1186/1476-069X-9-37, 2010.
González-Bailón, S., Wang, N., Rivero, A., Borge-Holthoefer, J., and
Moreno, Y.: Assessing the bias in communication networks sampled from
Twitter, Social Networks, 38, 16–27, 2012.
Guo, B., Yu, Z., Zhou, X., and Zhang, D.: From participatory sensing to mobile
crowd sensing, in: Pervasive Computing and Communications Workshops (PERCOM
Workshops), 2014 IEEE International Conference on, 593–598, IEEE, 24–28 March 2014, Budapest, Hungary, 2014.
Hastie, T. J. and Tibshirani, R. J.: Generalized additive models, vol. 43, CRC
Press, London, UK, 1990.
Herfort, B., Albuquerque, J. P. d., Schelhorn, S.-J., and Zipf, A.: Does the
spatiotemporal distribution of tweets match the spatiotemporal distribution
of flood phenomena? A study about the River Elbe Flood in June 2013, in:
International Conference on Information Systems for Crisis Response and
Management, 11, 18–21 May 2014, The Pennsylvania State University,
University Park, Pennsylvania, USA, 2014.
Hughes, A. L., St Denis, L. A., Palen, L., and Anderson, K. M.: Online public
communications by police & fire services during the 2012 Hurricane Sandy,
in: Proceedings of the 32nd annual ACM conference on Human factors in
computing systems, 26 April–1 May 2014, Toronto, Canada, 1505–1514, ACM, 2014.
Kim, E. J.: The impacts of climate change on human health in the United
States: A scientific assessment, by uS Global change research program, J. Am.
Plann. Assoc., 82, 418–419, 2016.Kryvasheyeu, Y., Chen, H., Obradovich, N., Moro, E., Van Hentenryck, P.,
Fowler, J., and Cebrian, M.: Rapid assessment of disaster damage using social
media activity, Science advances, 2, e1500779, 10.1126/sciadv.1500779, 2016.McCarthy, M. P., Best, M. J., and Betts, R. A.: Climate change in cities due to
global warming and urban effects, Geophys. Res. Lett., 37, L09705, 10.1029/2010GL042845, 2010.
Merrifield, N. and Panechar, M.: Uncertainty Reduction Strategies via Twitter:
The 2011 wildfire threat to Los Alamos National Laboratory, in: Proceedings
from AEJMC Annual Conference, 9–12 August 2012, 2012. Chicago, IL, USA, 2012.
Morabito, M., Profili, F., Crisci, A., Francesconi, P., Gensini, G. F., and
Orlandini, S.: Heat-related mortality in the Florentine area (Italy) before
and after the exceptional 2003 heat wave in Europe: an improved public health
response?, Int. J. Biometeorol., 56, 801–810, 2012.
Naughton, M. P., Henderson, A., Mirabelli, M. C., Kaiser, R., Wilhelm, J. L.,
Kieszak, S. M., Rubin, C. H., and McGeehin, M. A.: Heat-related mortality
during a 1999 heat wave in Chicago, Am. J. Prev. Med.,
22, 221–227, 2002.
Olteanu, A., Castillo, C., Diaz, F., and Vieweg, S.: CrisisLex: A Lexicon for
Collecting and Filtering Microblogged Communications in Crises, in: ICWSM,
8th International Conference on Weblogs and Social Media, ICWSM 2014,
1–4 June 2014, Ann Arbor, USA, Code 114731, 2014.
Olteanu, A., Vieweg, S., and Castillo, C.: What to expect when the unexpected
happens: Social media communications across crises, in: Proceedings of the
18th ACM Conference on Computer Supported Cooperative Work & Social
Computing, 14–18 March 2015, Vancouver, Canada, 994–1009, ACM, 2015.Preis, T., Moat, H. S., Bishop, S., Treleaven, P., and Stanley, H. E.:
Quantifying the digital traces of Hurricane Sandy on Flickr, Sci. Rep., 3,
3141, 10.1038/srep03141, 2013.
Procopio, C. H. and Procopio, S. T.: Do you know what it means to miss New
Orleans? Internet communication, geographic community, and social capital in
crisis, J. Appl. Commun. Res., 35, 67–87, 2007.
Semenza, J. C., Rubin, C. H., Falter, K. H., Selanikio, J. D., Flanders, W. D.,
Howe, H. L., and Wilhelm, J. L.: Heat-related deaths during the July 1995
heat wave in Chicago, New Engl. J. Med., 335, 84–90, 1996.
Smith, B. G.: Socially distributing public relations: Twitter, Haiti, and
interactivity in social media, Public Relat. Rev., 36, 329–335, 2010.
Starbird, K., Palen, L., Hughes, A. L., and Vieweg, S.: Chatter on the red:
what hazards threat reveals about the social life of microblogged
information, in: Proceedings of the 2010 ACM conference on Computer supported
cooperative work, 6–10 February 2010, Savannah, Georgia, USA, 241–250, ACM,
2010.Steadman, R. G.: A universal scale of apparent temperature, J. Clim. Appl.
Meteorol., 23, 1674–1687, 1984.
Sutton, J., Palen, L., and Shklovski, I.: Backchannels on the front lines:
Emergent uses of social media in the 2007 southern California wildfires, in:
Proceedings of the 5th International ISCRAM Conference, 4–7 May 2008,
Washington, DC, USA, 624–632, 2008.
Vieweg, S., Hughes, A. L., Starbird, K., and Palen, L.: Microblogging during
two natural hazards events: what twitter may contribute to situational
awareness, in: Proceedings of the SIGCHI conference on human factors in
computing systems, 10–15 April 2010, Atlanta, GA, USA, 1079–1088, ACM,
2010.
Watson, H. and Finn, R. L.: Social media and the 2013 UK heat wave:
opportunities and challenges for future events, in: Proceedings of the 11th
International ISCRAM Conference, 18–21 May 2014, University Park,
Pennsylvania, USA, 2014.
Watts, D. J.: The virtual lab, in: Proceedings of the sixth ACM international
conference on Web search and data mining, 4–8 February 2013, Rome, Italy,
1–2, ACM, 2013.
Xu, Z., Sheffield, P. E., Su, H., Wang, X., Bi, Y., and Tong, S.: The impact
of heat waves on children's health: a systematic review, Int. J.
Biometeorol., 58, 239–247, 2014.
Yates, D. and Paquette, S.: Emergency knowledge management and social media
technologies: A case study of the 2010 Haitian earthquake, Int. J. Inform.
Manage., 31, 6–13, 2011.