=Paper=
{{Paper
|id=Vol-1329/papersmile_5
|storemode=property
|title=Visual Analysis of Real-time Social Media for Emergency Response
|pdfUrl=https://ceur-ws.org/Vol-1329/papersmile_5.pdf
|volume=Vol-1329
|dblpUrl=https://dblp.org/rec/conf/esws/MazumdarLIC14
}}
==Visual Analysis of Real-time Social Media for Emergency Response==
https://ceur-ws.org/Vol-1329/papersmile_5.pdf
Visual Analysis of Real-time Social Media for
Emergency Response
Suvodeep Mazumdar, Vitaveska Lanfranchi, Neil Ireson, and Fabio Ciravegna
Organisations, Information and Knowledge Group,
Department of Computer Science,
University of Sheffield, UK
{s.mazumdar, v.lanfranchi, n.ireson, f.ciravegna}@dcs.shef.ac.uk
Abstract. The prevalence of Social Media in sharing day to day in-
formation regarding all aspects of our life is ever increasing. More so,
with access to cheap Internet-enabled devices and proliferation of So-
cial Media applications. Among the variety of information shared, the
most relevant, in the context of this paper is how individuals assess their
surroundings and how they or their loved ones are a↵ected by adverse
events, disasters and crises. Traditional channels of communication often
fall behind in providing timely information for emergency responders to
formulate an accurate picture of the situation on the ground. The role
of Social Media in complimenting such sources of information is thus
invaluable and Social Media has been recognised as a key element of
assessing evolving situations. Timely, accurate and efficient means to ex-
plore and query Social Media is essential for an e↵ective response during
emergencies, and hence this gives rise to a Knowledge Management issue.
Our paper presents our approach to analysing Real-Time Social Media
data streams using Visual Analytic techniques. We discuss the highly
visual and interactive approach we employ to provide emergency respon-
ders means to access data of interest, supporting di↵erent information
seeking paradigms.
Keywords: Social Media, Emergency Response, Visual Analytics
1 Introduction
The challenge of gathering a good understanding of large volumes of Social Me-
dia data streams is a significant one. The nature of Social Media itself, being
highly dynamic, multi-lingual, geographically distributed, and highly relevant
to the short-term zeitgeist poses enormous challenges. Additionally, the highly
repetitive and noisy nature of Social Media also adds significant challenges to
analysis e↵orts. In spite of these challenges, the potential of Social Media is im-
mense and has been recognised as significant in Emergency Response by various
organisations. Social Media empowers analysts with better means to understand
public perception of events and situations on the ground as well as facilitating
682 Mazumdar et al.
decision making processes for planning rescue operations12 [20, 31]. The Emer-
gency Response domain, owing to the need for the efficient delivery of critical
information for decision makers, requires the assimilation, analysis and visuali-
sation of Real-Time information. How such information can be presented to end
users is a significant research challenge, as evolving situations require means to
ensure the dynamicity of information is communicated. Social Media is multi-
dimensional and hence, the information delivered to users must be communicated
in a multi-faceted paradigm.
The goal of our research is to facilitate exploration of large volumes of Social
Media information for gathering a very quick understanding of a situation at
hand. It is important to note that this paper discusses events and situations
from a broader perspective – events refer to anything that occurs3 that is of
interest to an analyst, while situations relate to the conditions and state of
a↵airs (including events that occur to shape situations). To this end, we have
developed a system, TUI (Tracking User Intelligence) [13] that exploits various
visualisations and employs di↵erent interaction paradigms and approaches to
help users improve their Situation Awareness [11, 32] by exploring various facets
of Social Media. The context of our paper is all types of emergencies (major and
minor) and events, but the e↵ectiveness of our solution depends on the volume
of Social Media generated. For example, low scale events such as small accidents
in less populated areas would generate less interest, and hence, minimal Social
Media posts. Our system also explores how users can have access to information
based on temporal windows and can observe the evolution of events.
This paper is structured as follows: the next section describes related work.
Section 3 presents an overview of the TUI interface. Section 4 describes how var-
ious types of information needs are addressed by elements within the framework,
and how they are relevant to Emergency Response. Section 5 briefly discusses
several field studies we conducted in real world examples and we conclude the
paper with lessons learned and a discussion of future work.
2 Related Work
We present related work in two areas: Situational awareness and Visual Ana-
lytics. Situational Awareness in emergencies is paramount to deliver a timely
and e↵ective response [9]. To achieve e↵ective Situational Awareness, emergency
services must collate information from multiple sources and use it to build an
understanding of the current situation and how this may evolve over time [11].
Leveraging data from citizens to build a form of collective intelligence [26], dur-
ing emergencies or for security purposes, is becoming a vital resource for Situa-
tion Awareness [22]. During the 2007 southern California wildfires, two bulletin
boards were set up to facilitate the exchange of information between citizens
1
http://www.unocha.org/top-stories/all-stories/
disaster-relief-20-future-information-sharing-humanitarian-emergencies
2
http://www.unocha.org/hina
3
http://www.complexevents.com/2011/08/23/event-processing-glossary-version-2-0/
Visual Analysis of Real-time Social Media for Emergency Response 3
69
and authorities [24]. A later analysis of Twitter postings during the 2009 Red
River flooding [27] indicated that the service was being used by citizens and
communities to collate and propagate information in a concise and responsive
manner. Several systems have been developed to support citizen participation
during emergencies that either directly foster data from citizens through custom
apps [19] or analyse public data stream to extract real-time knowledge [2,29,30].
Existing techniques for searching Social Media involve exploiting entity-based
semantic features [28]; entity mentions, hashtags, URLs and metadata [17]; and
entity annotations coupled with user models for personalised searches [1]. Rec-
ommendations and filtering systems are used to help users reduce information
overload, i.e. recommending links that users may find interesting; using dynamic
semantic models of user interests [2]; recommending posts and friends based on
categories [10, 21].
Visual Analytic techniques have been proposed to represent and filter Social
Media at di↵erent levels of specificity [15] [4] and to convey information evolu-
tion in the crisis management domain [23]. When visualising large scale Social
Media data, Visual Analytics is mainly used to provide high level overviews. [14]
explores information regarding Social Media campaigns, [23] uses Twitter to un-
derstand the progression of earthquakes and [33] explores trends in emergency
medicine. While these systems manage to efficiently display the chosen informa-
tion, they are limited in the amount of data displayed. Systems with a broader
focus try to capture the properties of generic data, allowing users to filter the
data to items of interest. [15] for example, is a system for visualising and sum-
marising events on Twitter. [8] allows users to explore Real-Time data streams
relating to a given keyword. [3] is a system that improves Situation Awareness
during small-scale crisis response, such as factory fires or music festivals by fo-
cussing on geotagged tweets and employing classification algorithms to identify
messages relevant to specific events. Whilst most Social Media visualisation ap-
proaches rely on geographical and temporal features such as [18], some systems
are starting to exploit the semantic of the data to enhance the visualisations.
Examples of such systems are [6, 12, 15]. [15] uses features such as sentiment
and link popularity to geographically plot the data. [12] uses features such as
sentiment to create news flow diagrams that analyses the evolution of keywords
and sentiments over time. [5] also focuses on interactive colour-coded timeline
displays. [6] cluster groups of users and their evolution over time for a particular
topic.
3 Tracking User Intelligence – An Overview
We designed our solution centering to the di↵erent modes of Information Seeking
proposed by Bates [7]. Figure 2 presents the four modes: Searching, Monitoring,
Browsing and Being Aware.
TUI is a multi visualisation platform that consists of multiple views over
di↵erent facets of Social Media data. Users can interact with the system via
each visualisation widget or a set of generic query elements to define filters. The
704 Mazumdar et al.
user interface is separated from the backend processing, in order to deliver a
more responsive performance, and hence our solution consists of two independent
solutions. Several interaction paradigms govern how users can interact with the
system, and therefore, provides multiple ways of engaging with the data. While
being founded on the four information seeking models of Bates, TUI supports
Schneiderman’s well-known information seeking paradigm “overview first, zoom
and filter, and details on demand” [25].
Fig. 1. A Screenshot of the developed system, Tracking User Intelligence (TUI). Five
main sections can be observed (A: Contextual Information Visualisation; B: Content
Presentation; C: Filters; D: Current Selection/filters; and E: Real Time Updates)
Figure 1 shows the four main components of the TUI interface. The interface
is designed to enable users simultaneously access the most important information
that is relevant to their present session. We enable this by providing a set of
tabs (Section A: Contextual Information Visualisation), each presenting a piece
of contextual information depending on the user’s information need. The tabs
(most relevant ones are discussed in the paper) are as follows: Map View (‘Map’),
Timeline View (‘Timeline’), Graph view (‘Graph’), Chart View (‘Chart’), Trend
View (‘Trend’), Freq View (‘Popular’). Map view provides contextual spatial
information on a geographical map, while timeline provides visual summaries on
a temporal scale. Graph view provides topical information, presented as a graph
of topical relation among Social Media messages, where each topic is visually
encoded based on the number of occurrence, associated collective sentiment or
any other parameter that is decided as appropriate. The type of graph that is
deployed depends on the particular use case, and TUI can select from a set of
Visual Analysis of Real-time Social Media for Emergency Response 71
5
candidate graphs based on the number of topics. For example, a typical node-
link graph can present a lower number of topics, but can provide an easier
way of communicating relational information between co-occurring topics. The
Context and Hierarchy chain [16], on the other hand presents a larger number
of topics along with their hierarchical relations but requires a greater amount of
interactivity to understand co-occurring topics. Chart view provides an overview
of the entire dataset, presented as a series of pie charts. Trend view and Freq view
provide advanced scatterplot visualisations to display which topics are trending
and the most shared Social Media posts. The last two views are hidden from a
typical Emergency Responder’s profile, as they are presently under development
and more investigation is needed into the best possible visualisations. These
contextual tabs address the first part of Schneiderman’s paradigm ’overview
first’.
The Social Media posts section (Section B: Content Presentation) shows the
data instances, where each Social Media post is presented as a snippet, chrono-
logically listed. The display of Social Media posts is designed to address the last
part of Schneiderman’s ‘details-on-demand’ paradigm, where the session of user
exploration dictates the relevant content to be displayed. The central part of
Schneiderman’s paradigm, ‘zoom and filter’ is the final filtering section (Section
C: Filters). The filtering interface provides two functions – visual communication
and overviews, and filtering. This section presents tag clouds of several facets of
the data such as authors, places, type of post (photo, video, audio, link etc.), key-
word or hashtag. Additionally, other interaction mechanisms such as text entry
boxes, sliders, calendar widgets and buttons are presented to users for entering
specific filters (or queries). Overall, interactions within Section A and C result
in defining the context of the user’s information needs and eventually retrieve
the content for Section B. The Real-Time Updates section (Section E) provides
a real-time reflection of the background data collection: this informs the user
that in the time that has passed since the last time the visualisations have been
refreshed, there has been a number of new messages that have been collected.
The user has three choices: ignore the message, view only the new messages,
or view all the data that has been collected. The final component presents the
filters and selections that are active on the present exploration session of the
user. These filters are generated by the user by interacting with visualisations
and interface elements from the Sections A and C. Users can disable each filter
by clicking on the (x) button if required.
4 Supporting Multiple Modes of Information Seeking
Within the scope of Emergency Response, Searching involves directed and ac-
tive information seeking activities, where the user has a very specific information
need, and knows the information exists. For example, an emergency responder
is investigating a situation at hand (such as a flood) and is looking for more in-
formation related to the state of rivers overflowing. Monitoring involves the user
having a specific information need, but is unsure if the information exists and
6
72 Mazumdar et al.
hence monitors for relevant information. For example, an emergency responder
is aware that flooding is a regular occurrence in a city, and is monitoring the
condition of the city with a higher than usual predicted rainfall. Browsing, on
the other hand involves the user actively looking for any relevant information,
but in an undirected manner. An example of browsing would be an emergency
responder looking for any occurrence of floods within a large geographical area.
Being aware involves the user being passively looking for relevant information
in an undirected manner. For example, an emergency responder looking for any-
thing which can indicate an incident occurring that might be of interest to be
further investigated.
Fig. 2. Four modes of Information Seeking, proposed by [7]
As explained previously, Schneiderman’s information seeking paradigm was
central to the design of the interface. Bate’s information seeking model provided a
basis for understanding how users may need to search/explore/monitor existing
situations using the system. During initial design phases, Bate’s model along
with interviews and focus groups with emergency responders and practitioners
provided realistic scenarios that can explain what are the information needs of
users at di↵erent stages of an emergency situation. This section presents four
main scenarios that relate to the di↵erent elements of Bate’s model, and how
TUI can be typically used to identify data of interest. It is to be noted that
while all of the aspects of the model requires active searching, the manner in
which the user can trigger the searches and the follow up tasks play a role in
addressing di↵erent types of information needs.
The filters section (Section C) is key to this task, where the user can drill-
down to a set of data instances of interest, based on the information need of the
user. The selection can be done using di↵erent interaction techniques and via a
variety of facets. The variety of facets can be observed on the accordion menu
on the right – three levels of filters are provided to users. Figure 3 describes how
these filters are organised - Content filters, Immediate Context filters and Wider
Context filters. Information that can be easily extracted from the content of the
Social Media posts are referred to Content Filters. The content filters are the
features that are aligned the closest to the Social Media post itself. A minimal
Visual Analysis of Real-time Social Media for Emergency Response 73
7
analysis of the Social Media posts can provide a few more features, which have
been organised into Immediate Context. While some features like Language or
Type can be easily retrieved from the data provider, they may require some basic
analysis to interpret content and infer. The Wider context filters indicate the
features that require a further level of analysis/interpretation/query. Features
such as identifying locations may be easily provided by geolocated posts, but
the process may be considerably more difficult if there is a need to analyse text
to interpret locations. Information surrounding a post such as the relevant users
(who posted, who replied or who were mentioned) can be retrived by further
queries to the data providers and may require further processing.
Fig. 3. Three levels of filters: Content filters indicate elements that can be quickly
retrieved from the content of the Social Media posts; Immediate Context filters indicate
information that can be inferred from the Social Media posts with minimal analysis;
and Wider Context filters refer to the information that can be derived from the user
who are relevant to the post, or the locations related to the posts.
A combination of filters can be used to identify an initial subset of the data,
and then users can progressively apply further filters to reach the data instances
that are of interest. For example, if the user is interested in the negative/positive
messages that have had a high impact among the users, he/she can make a
combination of selections of emotions/sentiments as well as selection of a high
number of repeated messages. Observing the hashtags or keywords can then
provide a rough summary of the topics of discussions and provide a greater
overview of the situation.
4.1 Searching – Active and Directed
The first, and possibly most relevant to most Semantic Web applications is the
ability to search for existing information, when there is a highly specific infor-
mation need. Within the context of Emergency Response, the need to search for
information, plays a significant role once an incident has been identified. Another
8
74 Mazumdar et al.
role searching plays is post-event analysis where all data is searched to under-
stand how an event evolved from di↵erent perspectives. E↵ectively, searching
provides answers to questions that are already known by analysts and to facili-
tate follow-on analyses. In order to perform searching tasks, users simply enter
(or select) an appropriate set of filters and reach the data of interest. Section C
(filtering section) is the most appropriate in this context, as it provides direct
means for users to search. Interactions within Section A (visualisations) also gen-
erate filter queries but such features are more pertinent to browsing activities.
The search terms that are involved in this process are highly dependent on the
evolution of the event as well as the event itself. Hashtags and keywords vary on
how they have propagated within Social Media, and therefore, it is difficult to
predict which terms would best suit the scenario being investigated. This calls
for the need to monitor situations based on an initial ‘guess’ to encapsulate pos-
sible terms, and then fine-tuning queries to capture and retrieve more relevant
information.
4.2 Monitoring – Passive and Directed
Perhaps most relevant to Emergency Response is the notion of monitoring, where
the analyst needs to monitor an evolving situation, and looks out for information
that may be relevant. This is achievable by setting queries and filters, and dur-
ing standard data exploration via visualisations (Section A) and reading Social
Media content (Section B), users can be updated when new relevant content is
made available after harvesting recent Social Media posts. Once new content is
available that is relevant to the present search criteria, users are communicated
by a clickable label which states ‘X new messages available’. The analyst is faced
with one of three options in such situation: the first being, continue exploration
and ignore the update. This would have no e↵ect on the present exploration,
and the user can proceed with finishing his/her analysis. With more informa-
tion being made available, the notification is updated. This is enabled/disabled
via the dropdown (session type) selection option ‘Static’ (the other options are
‘dynamic’ and ‘batch-update’). The second option the user has is to proceed
with analysing only the new content that has been retrieved. This notes the
time when the previous analysis had started (the time when the last query was
triggered). Selecting ‘batch-update’ from the dropdown enables this option and
the user can then click on the button to view the relevant data instances that
have been recently added. The third option the user has is to proceed analysing
all of the data, with the new instances added to the analysis. This is performed
by selecting ‘static’ and clicking on the click-able label.
The last option is a dynamic monitoring option, which regularly updates with
new posts continually appearing during exploration sessions. This options is the
least used, and hence is presently not encouraged to be used – more work is
needed to understand how continuously evolving data can be presented to users
without causing confusion and loss of analysis e↵ort.
Visual Analysis of Real-time Social Media for Emergency Response 9
75
4.3 Browsing – Active and Undirected
Browsing involves users looking for information as and when their interests
evolve. This is a mode which is also highly relevant to Emergency Response.
Monitoring continuously evolving data, and searching existing data can indicate
interesting events, keywords, or hashtags that might be relevant to improve the
understanding of an evolving situation. In our approach, interactive Visualisa-
tions are key to this: while exploring a relevant dataset, users have the possibility
to add new filtering terms by clicking elements from tag clouds, charts, timelines
or maps. Clicking on sensitive areas of the visualisations immediately triggers
queries, which result in a new filter being added and the system to drill-down
into a more relevant subset of the data being explored. The user can remove the
recently added filter by deselecting the filter from Section D (Current Selection).
4.4 Being Aware – Passive and Undirected
The most complex of information seeking modes is the state of being aware. TUI
facilitates this by easily combining some of the approaches that are employed
in the other three modes. The state of being aware in Emergency Response
implies that an analyst is aware of the wider context of his/her analytic activity,
but unaware of what event/situation may arise. This is often perceived to be
the precursor to some of the modes and is conducted as a part of an initial
or continued survey of the broader area (geographic, temporal or topical). In
a typical ER (Emergency Response) activity, it is expected that an instance of
the interface is continuously dynamically updated to provide overviews, based
on query terms that are most likely to retrieve interesting events – generic terms
such as ‘flood’, ‘accident’, ‘911’ or ‘fire’ could form a good candidate set of
keywords to look for4 . Identifying any relevant interesting event would then
initiate monitoring, browsing and searching activities on other instances of TUI.
5 Architecture and Implementation
The TUI interface is the second part of two systems - the first being a So-
cial Media harvesting system which enables gathering of Social Media posts,
and the second being a system which interfaces with the harvesting system and
a local data store. While the first system (harvester) involves querying multi-
ple sources of data continuously for new information and storing the results in
local datastores following several iterations of backend processing, the second
(TUI) is the interactive interface that users can use to access the data being
stored/analysed/harvested. TUI is implemented as a standard web application,
and written completely in HTML and Javascript. Several toolkits have been used
4
It is to be noted that several of such ‘generic’ terms can also occur in posts that are
highly irrelevant such as song lyrics, quotes from speeches etc. The decision to select
relevant terms is mostly left with the analysts, based on their interpretation of the
content being currently monitored.
76
10 Mazumdar et al.
within TUI to facilitate interactions such as jQuery5 , and provide customised
look and feel such as jQueryUI6 , Less7 and BlockUI8 . Visualisations are provided
by D3.js9 , Highcharts10 , Google Maps11 and Javascript Infovis toolkit12
6 Discussions and Continuing Work
Over the summer of 2013, as a part of multiple projects, TUI was used to mon-
itor several large events across the UK. Several Emergency Response organisa-
tions, Police, City Councils, event organisers and authorities were involved in
the events. During the events the di↵erent types of information seeking modes
were employed, and the system was used to monitor by several analysts at the
same time. One of the key findings was the need for supporting multiple types of
information seeking at the same time. Analysts need to explore, query, browse
and be aware of situations at the same time, and hence, several instances of TUI
are necessary to be active for improving Situation Awareness.
The TUI system is currently being redesigned to provide support for mul-
tiple tasks within one interface. Several layouts are presently being evaluated
to understand which layout from a set of candidate layouts would be the most
e↵ective. The system is also planned to be evaluated with emergency responders
during planned and unplanned events. Several techniques such as focus group,
contextual inquiry and shadowing are planned to be used in the evaluations to
understand how the system compliments traditional techniques for Emergency
Response.
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