WORKSHOP REPORT

Report on the Second Workshop on Exploitation of Social
Media for Emergency Relief and Preparedness(SMERP 2018) at the Web Conference (WWW) 2018
Saptarshi Ghosh
Indian Institute of Technology, Kharagpur, India
saptarshi@cse.iitkgp.ac.in
Kripabandhu Ghosh
Indian Institute of Technology, Kanpur, India
kripa@cse.iitk.ac.in
Tanmoy Chakraborty
IIIT Delhitanmoy@iiitd.ac.in

Debasis Ganguly
IBM Research Labs, Dublin, Irelanddebasis.ganguly1@ie.ibm.com

Gareth J. F. Jones
Dublin City University, Ireland
Gareth.Jones@dcu.ie

Marie-Francine Moens
Katholieke Universiteit Leuven, Belgium
marie-francine.moens@cs.kuleuven.beAbstract
The Second Workshop on Exploitation of Social Media for Emergency Relief and Preparedness(SMERP) was held in conjunction with The Web Conference (WWW) 2018 at Lyon, France. A
primary aim of the workshop was to promote multi-modal and multi-view information retrieval fromthe social media content in disaster situations. The workshop programme included keynote talks, apeer-reviewed paper track, and a panel discussion on the relevant research problems in the scope ofthe workshop.

1Introduction and Motivation

User-generated content on online social media (OSM) platforms such as Twitter , Facebook , WhatsApp,
has become an important source of real-time information during emergency events (e.g. natural disasters such as earthquakes, cyclones, floods, fire, epidemics or man-made disasters such as terror attacks,riots) [4, 8]. During such events information posted on OSM platforms can contribute significantly
to relief operations. Additionally, crowdsourced content created using OSM platforms can also beutilised for emergency preparedness, such as for identifying disaster-prone regions and infrastructures,developing early warning systems, developing emergency-resilient communities, and so on.1
Given the huge volume and the rapid rates at which content is posted on OSM platforms, automatedtechniques and information systems need to be developed for extracting, summarizing and presenting
critical information in a timely and useful way. The Exploitation of Social Media for Emergency Relief
and Preparedness (SMERP) workshop series aims to provide a forum for researchers working in fields
related to this topic to gather, to present and discuss their findings, and to explore the directions
for further developments in this area. Our workshops are targeted at bringing together researchers
from diverse fields – Information Retrieval, Data Mining and Machine Learning, Natural Language
1

Note: the terms “emergency” and “disaster” are used interchangeably in this note.

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Processing, Machine Translation, Social Network Analysis, Computational Social Science, Human
Computer Interaction – who can potentially contribute to utilising social media for emergency relief
and preparedness. The SMERP workshop series also aims to provide the research community withreal-life datasets to foster research on the theme.
The first SMERP workshop was held in in conjunction with ECIR 2017 [3]. SMERP 2018 was
the second edition of the workshop was held in conjunction with the Web Conference (WWW) 2018
in Lyon, France (https://www.cse.iitk.ac.in/users/kripa/smerp2018/) . SMERP 2018 included
two keynote talks, a peer-reviewed research paper track, and a panel discussion. Additionally, theSMERP organizers released a dataset for use by the research community.

2Keynote Talks

The workshop had two keynote talks by eminent researchers working on topics at the centre of theinterests of the SMERP workshop.
(1) Utility of Social Media Information in Response to Natural Disasters
Speaker: Prasenjit Mitra, Professor of Information Sciences and Technology and Associate Dean for
Research, The Pennsylvania State University, U.S.A.
Abstract: In this talk Professor Mitra explored the use social media information to improve responses
to natural disasters. Drawing from his own experiences using AIDR, a tool that enables information
gathering for disaster response, he showed how social media information can be used effectively tohelp in disaster response. His work in this area addresses questions of how tweets can be classified inreal-time using volunteers who assist in training a machine learning based system. He has exploredwhether and how his existing datasets from previous disasters can be utlized to effectively enabledisaster relief. He then showed how reports can be generated in natural language utilizing informationfrom the tweets; these reports must be generated to satisfy the different perspectives and informationneeds of different first responders and volunteers who help orchestrate and co-ordinate disaster relief
efforts. Finally, he outlined how images can be processed automatically and to help improve theresponse. He concluded by examining issues related to the veracity of the information obtained during
disasters and the limits of using information from social media with an eye towards the future.
(2) Transforming Social Big Data into Timely Decisions and Actions for Crisis Mitigation
and Coordination
Speaker: Amit Sheth, Executive Director of Kno.e.sis, Wright State University, U.S.A.
Abstract: Crises are imposing massive costs to economies worldwide. Natural disasters caused record$306 billion in damage to the U.S. in 2017! Real-time gathering of relevant data through the now
ubiquitously present mobile technologies, and the ability to disseminate it hrough social media has
forever changed how disaster and health crisis monitoring and response are carried out. Both traditional crisis response organizations as well as temporary, informal, self-organized and community-basedorganizations have come to rely increasingly on social media. Furthermore, the ability to collect, repurpose and reuse data from past events is helping with preparedness and planning for future events.
In this talk, Professor Sheth reviewed his extensive experience of: (a) interactions with a variety of
stakeholders involved in emergency response at city, county, country and international levels, (b) research on real-time social media analysis spanning spatio-temporal-thematic; people-content-network;
linguistic-sentiment-emotion-intent analysis dimensions, (c) development and use of crisis response
specific tools (location identification, demand-supply match) and the comprehensive Twitris semanticsocial intelligence system (which is also commercialized as Cognovi Labs), and (d) a variety of realworld evaluations and real-time uses (e.g., supplying data for Google Crisis map during UttarakhandFloods, rescue during Kashmir Floods, neighborhood image map during Chennai Floods, providing
information to FEMA during Oklahoma tornados), spread of disease and epidemiology (e.g., Zika
spread), metro-level multi-agency disaster preparedness exercise, etc.

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3

Peer-reviewed Research Paper Track

The workshop solicited original contributions related to relevant topics, which included (but was notlimited to):• Multi-modal and multi-view Information Retrieval• Addressing the code-mixed and noisy, informal vocabulary of OSM content• Transfer learning - applying models trained on prior emergency event(s) on a future event• Detection of events and emerging themes• Real-time management and summarization of dynamic content streams• Detection of rumours and false news, verification of news and identification of trustworthy sources• Geo-tagging and geo-localisation of content and sources• Social network models for information diffusion in emergency situations• Identifying disaster-prone or accident-prone regions and infrastructures• Designing crowdsourcing systems for emergency preparedness and post-disaster relief• Building knowledge bases for automatically mining social media posts during emergency
Nine (9) papers were submitted, out of which five (5) papers were accepted for presentation at theworkshop, through a rigorous peer-review process (the Technical Program Committee is detailed at
the workshop website).
We now briefly describe the contributions of the accepted papers. Kumar et al. [5] modelled
the evacuation behaviour of the residents of New York City in the aftermath of Hurricane Sandy.
Moumtzidou et al. [6] combined textual and visual information to determine the relevance of social
media content with respect to flooding. Ghosh et al. [2] reported that class specific TF-IDF boosting
improves classifier performance on microblogs posted during disaster events. Soni et al. [7] studied theincompleteness of the gold standard created for a shared task relevant to the workshop theme (theFIRE 2017 IRMiDis track), and provided suggestions for the creation of more robust gold standard forsuch tasks. Finally, Dutt et al. [1] developed a real-time system that identifies locations using natural
language processing techniques on the microblog text during disaster situation. The details of thepapers are as below.
Title: Utilizing Geo-tagged Tweets to Understand Evacuation Dynamics during Emergencies: A casestudy of Hurricane Sandy
Abstract: Hurricane evacuation is a complex process and a better understanding of the evacuation
behavior of the coastal residents could be helpful in planning better evacuation policy. Traditionally, various aspects of the household evacuation decisions have been determined by post-evacuationquestionnaire surveys, which are usually time-consuming and expensive. Increased activity of users
on social media, especially during emergencies, along with the geo-tagging of the posts, provides anopportunity to gain insights into user’s decision-making process, as well as to gauge public opinionand activities using the social media data as a supplement to the traditional survey data. This paper
leverages the geo-tagged Tweets posted in the New York City (NYC) in wake of Hurricane Sandy tounderstand the evacuation behavior of the residents. Based on the geo-tagged Tweet locations, weclassify the NYC Twitter users into one of the three categories: outside evacuation zone, evacuees,
and non-evacuees and examine the types of Tweets posted by each group during different phases of
the hurricane. We establish a strong link between the social connectivity with the decision of the users
to evacuate or stay. We analyze the geo-tagged Tweets to understand evacuation and return timeand evacuation location patterns of evacuees. The analysis presented in this paper could be useful forauthorities to plan a better evacuation campaign to minimize the risk to the life of the residents of theemergency hit areas.
Authors: Dheeraj Kumar and Satish Ukkusuri
Affiliation: Purdue University, United States

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Title: Flood relevance estimation from visual and textual content in social media streams
Abstract: Disaster monitoring based on social media posts has raised a lot of interest in the domainof computer science the last decade, mainly due to the wide area of applications in public safety andsecurity and due to the pervasiveness not solely on daily communication but also in life-threating situations. Social media can be used as a valuable source for producing early warnings of eminent disasters.
This paper presents a framework to analyse social media multimodal content, in order to decide if thecontent is relevant to flooding. This is very important since it enhances the crisis situational awarenessand supports various crisis management procedures such as preparedness. Evaluation on a benchmark
dataset shows very good performance in both text and image classification modules.
Authors: Anastasia Moumtzidou, Stelios Andreadis, Ilias Gialampoukidis, Anastasios Karakostas, Stefanos Vrochidis and Yiannis Kompatsiaris
Affiliation: Information Technologies Institute, CERTH, Greece
Title: Class Specific TF-IDF Boosting for Short-text Classification
Abstract: Proper formulation of features plays an important role in short-text classification tasks as
the amount of text available is very little. In literature, Term Frequency - Inverse Document Frequency(TF-IDF) is commonly used to create feature vectors for such tasks. However, TF-IDF formulation
does not utilize the class information available in supervised learning. For classification problems, if it
is possible to identify terms that can strongly distinguish among classes, then more weight can be given
to those terms during feature construction phase. This may result in improved classifier performancewith the incorporation of extra class label related information. We propose a supervised featureconstruction method to classify tweets, based on the actionable information that might be present,
posted during different disaster scenarios. Improved classifier performance for such classification tasks
can be helpful in the rescue and relief operations. We used three benchmark datasets containing tweets
posted during Nepal and Italy earthquakes in 2015 and 2016 respectively. Experimental results showthat the proposed method obtains better classification performance on these benchmark datasets.
Authors: Samujjwal Ghosh and Maunendra Sankar Desarkar
Affiliation: Indian Institute of Technology, Hyderabad, India
Title: Gold Standard Creation for Microblog Retrieval: Challenges of Completeness in IRMiDis 2017
Abstract: Microblogging sites like Twitter, Facebook, etc., are important sources of first-hand accounts
during disaster situations, and have the potential to significantly aid disaster relief efforts. The IRMiDis
track at FIRE 2017 focused on developing and comparing IR approaches to automatically identify andmatch tweets that indicate the need or availability of a resource, leading to the creation of a benchmark
dataset for future improvements in this task. However, based on our experiments, we argue that thegold standard data obtained in the track is substantially incomplete. We also discuss some reasons
why it may have been so, and provide some suggestions for making more robust ground truth data insuch tasks.
Authors: Ribhav Soni and Sukomal Pal
Affiliation: Indian Institute of Technology (BHU), Varanasi, India
Title: SAVITR: A System for Real-time Location Extraction from Microblogs during Emergencies
Abstract: We present SAVITR, a system that leverages the information posted on the Twitter microblogging site to monitor and analyse emergency situations. Given that only a very small percentageof microblogs are geo-tagged, it is essential for such a system to extract locations from the text of themicroblogs. We employ natural language processing techniques to infer the locations mentioned in
the microblog text, in an unsupervised fashion and display it on a map-based interface. The system
is designed for efficient performance, achieving an F-score of 0.81, and is approximately two orders ofmagnitude faster than other available tools for location extraction.
Authors: Ritam Dutt, Kaustubh Hiware, Avijit Ghosh and Rameshwar Bhaskaran
Affiliation: Indian Institute of Technology, Kharagpur, India
It is evident that the papers presented at the workshop included a mixture of works that developed
algorithms, works that modelled human behaviour during emergencies, and works that developed
systems for use during emergencies.

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Dataset Released

As stated earlier, the SMERP workshop series aims to provide the research community with reallife datasets to foster research on the theme. To this end, we made available (upon request) a large
dataset and invited interested participants to develop methods to solve some practical challenges overthe dataset. Specifically, we made available the following resources related to a particular disasterevent – the Nepal earthquake in April 2015: (1) A set of microblogs (tweets) posted during the event,(2) A set of images posted along with the tweets, and (3) A set of news media articles about the event,posted on various mainstream news media sites.
Interested participants could use this dataset to develop methods for tasks such as developing
algorithms for multi-modal information extraction, e.g., identifying information about infrastructuredamage from all the data sources.
We invite the research community to use the dataset for developing improved algorithms for utilizing
social media at the times of emergency. Those who are interested to get the dataset should follow theprocedure stated at the workshop website.

5Panel Discussion

The workshop ended with a panel discussion on the upcoming practical challenges related to use ofsocial media during emergencies. The panelists were our two invited speakers – Prof. Mitra and Prof.
Sheth, Prof. Niloy Ganguly (IIT Kharagpur, India) and Prof. Moens (KU Leuven, Belgium; also a
co-organizer of SMERP). Some of the questions discussed were: (1) How effective can models trainedon prior events be during a future event?, (2) Can social media be combined with other sources ofinformation for more effective extraction of critical information?, (3) How effective will be the use ofgeographical knowledge bases?

6Conclusion and Future Directions

Further details of the SMERP 2018 workshop can be found at the site (https://www.cse.iitk.ac.
in/users/kripa/smerp2018/). It is clear to us through the process of organizing this workshop thatthe topics related to the SMERP workshops are of significant interest to the research community.
Hence, we plan to continue organizing SMERP workshops in the coming years.

Acknowledgements
We are thankful to the Web Conference (WWW) for providing the opportunity to organize the workshop. The peer-review track would not have been possible without the support of the Program Committee members – the full list is available on the workshop website. We are also thankful to the authorsof the papers submitted to the workshop for considering SMERP.

References[1] Ritam Dutt, Kaustubh Hiware, Avijit Ghosh, and Rameshwar Bhaskaran. SAVITR: A system forreal-time location extraction from microblogs during emergencies. In Proc. of SMERP 2018, 2018.
[2] Samujjwal Ghosh and Maunendra Sankar Desarkar. Class specific tf-idf boosting for short-textclassification. In Proc. of SMERP 2018, 2018.
[3] Saptarshi Ghosh, Kripabandhu Ghosh, Debasis Ganguly, Tanmoy Chakraborty, Gareth J. F. Jones,and Marie-Francine Moens. ECIR 2017 Workshop on Exploitation of Social Media for Emergency
Relief and Preparedness (SMERP)). SIGIR Forum, 51(1):36–41, 2017.

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[4] Muhammad Imran, Carlos Castillo, Fernando Diaz, and Sarah Vieweg. Processing Social MediaMessages in Mass Emergency: A Survey. ACM Computing Surveys, 47(4):67:1–67:38, June 2015.
[5] Dheeraj Kumar and Satish Ukkusuri. Modeling evacuation behavior of nyc twitter users during
hurricane sandy. In Proc. of SMERP 2018, 2018.
[6] Anastasia Moumtzidou, Stelios Andreadis, Ilias Gialampoukidis, Anastasios Karakostas, Stefanos
Vrochidis, and Yiannis Kompatsiaris. Flood relevance estimation from visual and textual content
in social media streams. In Proc. of SMERP 2018, 2018.
[7] Ribhav Soni and Sukomal Pal. Gold standard creation for microblog retrieval: Challenges ofcompleteness in irmidis 2017. In Proc. of SMERP 2018, 2018.
[8] Sarah Vieweg, Amanda L. Hughes, Kate Starbird, and Leysia Palen. Microblogging During Two
Natural Hazards Events: What Twitter May Contribute to Situational Awareness. In Proc. ACM
SIGCHI, 2010.

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