Vol-3194/paper27
Jump to navigation
Jump to search
Paper
| Paper | |
|---|---|
 edit
| |
| description | |
| id | Vol-3194/paper27 |
| wikidataid | Q117344912→Q117344912 |
| title | Extracting Large Scale Spatio-Temporal Descriptions from Social Media |
| pdfUrl | https://ceur-ws.org/Vol-3194/paper27.pdf |
| dblpUrl | https://dblp.org/rec/conf/sebd/BonoP22 |
| volume | Vol-3194→Vol-3194 |
| session | → |
Extracting Large Scale Spatio-Temporal Descriptions from Social Media
Extracting Large Scale Spatio-Temporal Descriptions
from Social Media
Carlo Bono, Barbara Pernici
Politecnico di Milano, DEIB, Via Giuseppe Ponzio, 34, 20133 Milano, Italia
Abstract
The ability to track large-scale events as they happen is essential for understanding them and coordinating
reactions in an appropriate and timely manner. This is true, for example, in emergency management and
decision-making support, where the constraints on both quality and latency of the extracted information
can be stringent. In some contexts, real-time and large-scale sensor data and forecasts may be available.
We are exploring the hypothesis that this kind of data can be augmented with the ingestion of semi-
structured data sources, like social media. Social media can diffuse valuable knowledge, such as direct
witness or expert opinions, while their noisy nature makes them not trivial to manage. This knowledge
can be used to complement and confirm other spatio-temporal descriptions of events, highlighting
previously unseen or undervalued aspects. The critical aspects of this investigation, such as event
sensing, multilingualism, selection of visual evidence, and geolocation, are currently being studied as a
foundation for a unified spatio-temporal representation of multi-modal descriptions. The paper presents,
together with an introduction on the topics, the work done so far on this line of research, also presenting
case studies relevant to the posed challenges, focusing on emergencies caused by natural disasters.
Keywords
social media, spatio-temporal event description, information mining
1. Introduction
The nature of social networks implies an intrinsic distributional variability, since the topics on
social networks usually reflect, to different extents, events taking place in the world. Some items
transport relevant information about some aspect of reality, usually in the form of unstructured
and semi-structured data. For example, information about a specific event could be made
available using text in some language and/or images and videos, and further contextualized
with tags and conversations, extended with external links, and enriched with comments and
interactions. These different dimensions of social media posts contain information, explicitly or
implicitly, possibly related to actual events. “Possibly” since separating relevant1 and irrelevant
information is usually cumbersome, both because of input data quality and volume. Since we
aim at extracting descriptions of large-scale events along the dimensions of space and time, we
mainly focus on two operational aspects: the ability to automatically detect whether a certain
event is actually ongoing, and the ability to extract quantitative, actionable measures as the
SEBD 2022: The 30th Italian Symposium on Advanced Database Systems, June 19-22, 2022, Tirrenia (PI), Italy
$ carlo.bono@polimi.it (C. Bono); barbara.pernici@polimi.it (B. Pernici)
� 0000-0002-5734-1274 (C. Bono); 0000-0002-2034-9774 (B. Pernici)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings CEUR Workshop Proceedings (CEUR-WS.org)
http://ceur-ws.org
ISSN 1613-0073
1
Relevant as in “informative with respect to to some chosen event, kind of event or aspect of reality”
�event unfolds, possibly in an adaptive fashion. The former is meant to be used as a trigger for
preempting data collection tasks. The latter has to be compared and complemented with other
layers, such as sensor or forecast data, in order to enhance the understanding of reality. Both
aspects are affected by the variability of the social media data distribution over space and time,
and both can exploit the same variability.
The remainder of this work is structured as follows. In the next session, the main challenges
and topics in the literature are reviewed. Section 3 illustrates the investigations conducted so
far on these topics. Section 4 combines these results in an application customized for flood
events. Finally, Section 5 sketches future medium-term research directions.
2. Related work
The opportunities and challenges for using social media in emergency management have been
trending topics over the last years. Large-scale emergencies are not trivial to tackle with
automated and practical tools. We focus on emergencies since there is an essential value,
as well as they pose complex questions to be studied. The papers [1, 2] highlight recent
opportunities, challenges, applications and directions for the use of social media data for
emergency management at large. Vertical studies for the use of social media for specific events
have been performed, such as [3] for earthquake events and [4, 5] for the case of flood events.
Techniques for automatic image filtering on social network data, mainly using deep learning,
have been studied in the field of emergency and social sensing in [6, 7].
Methodologies and resources to analyze social media posts in multiple languages are also
essential, as discussed in [8] for lexicon building, and in [9] for an adaptive, cross-source
approach to social media crawling. How and to which extent social media can be integrated
into production-grade emergency management systems has been studied in [10] and [11]. Some
of the current work builds on past experience with emergencies, as documented in [12].
Although many studies explicitly address detection, filtering, geolocation and multilinguality
support, some limitations exist. Production-level requirements are hardly met for many kind
of events. Multi-modal approaches are sometimes investigated in literature, but not widely
adopted and usually limited to one of the tasks. The same holds true for data fusion approaches.
Our investigation aims at designing an end-to-end framework for automatically deriving event
characterizations, taking advantage of multi-modal and multi-source approaches in order to
solve these challenges. Benchmarking and comparison with existing solutions will be performed
on publicly available datasets and self-produced ones.
3. Issues and methods
3.1. Event detection and adaptation
Monitoring regions of the world for classes of events is one of the possible entry points for
social media data collection and processing. Some notable sources of information can dispatch
�alerts and forecasts, as it happens with GDACS2 and GloFAS3 .
In this context, we study both timeliness and geographical precision of information derived
from social media, in particular tweets, as we analyze it compared to information available in
authoritative sources. Not all events are covered with the same timeliness, so for large scale
events, we are using posterior validated data to understand to which extent social media can
support already available data, such as forecast and sensor data, as a detection mechanism.
As a first step, the use of dictionaries of query terms for social sensing is being evaluated.
For each of the dictionary terms, the time series of the term usage over a recent time window
can be obtained. This signal is then used to estimate if an event of interest is ongoing or not. A
first exploration of this methodology can be found in [13], in which we focused on searching
Twitter in many possible languages starting from a limited set of seed search keywords. We
are studying how to leverage available ground truth in order to automatically build language-
tailored resources. The current focus is on building language-centered dictionaries with a
data-centered approach4 , minimizing human intervention and taking advantage of the temporal
correlation between words and events. Feature windows built with these dictionaries are then
fed to a supervised classifier, such as a CNN, and evaluated in a leave-one-event-out fashion. Our
preliminary assessment shows that a low precision (<50%) and a good recall (~80%) triggering
system can be achieved, with almost no human intervention. This result is achieved with
negligible processing effort, and a comfortable request resolution.5
Figure 1: Occurrence of "landslide", compared with flood events in Nepal reported by GDACS
Detecting an event onset is only the first step towards awareness. Moreover, an efficient
detection mechanism provides a precious byproduct: broad matching keywords related to events
of interest. These, in principle, enhance the recall of the data collection. But as broad as they
can be, two issues should be considered. On one side, unpredictable descriptors, event-specific
qualities and linguistic variations are bound to happen. One significant example is given by
location names, which are not usually constant across events. On the other side, good "general
2
The Global Disaster Alert and Coordination System, started in 2004, is a joint initiative of the United Nations
Office for the Coordination of Humanitarian Affairs (UN OCHA) and the European Commission (EC).
3
The Global Flood Awareness System is a component of the Copernicus Emergency Management Service
(CEMS) that delivers global hydrological forecasts to registered users.
4
Leveraging language models, word embeddings and external data sources
5
One request per second https://developer.twitter.com/en/docs/twitter-api/rate-limits
�purpose" predictive features do not guarantee to match relevant contents only. In light of
this consideration, adaptive methods of data collection should be prioritized. Adaptability is
aimed at controlling the specificity of an event, together with its evolution over space and time.
An example of an approach that is both automated and adaptive is given by [9], focusing on
identifying variable search keywords over time.
3.2. Understanding and filtering semi-structured contents
Methods and tools that extract structured information from social media are the main focus of
this line of research. Among the general questions that can be posed, we include questions about
the category of posts, as in “is this post about <x>?”. These questions can be usually translated
to supervised classification tasks. The questions being asked could be related to some specific
semantic of the content, as in “is there a <y> in this image?”. Some of these questions could still
be mapped to classifications tasks, while some others relate to detection or segmentation tasks.
A systematic approach for answering this kind of questions has been proposed in [7], developing
the VisualCit6 image processing toolkit, aimed at extracting indicators about emergency events.
Here, image classification and object detection are usually achieved through the use of deep
neural networks, both with off-the-shelf solutions and custom trained networks. In this way,
specific queries can be posed to social media data, describing specific contents or situations,
such as “two or more persons” or “a flood event”. In addition, the ability to leverage user
feedback to adjust classifiers is also a topic of interest. Pre-processing actions such as removing
near-duplicates and filtering non-photographic material are also supported.
Figure 2: VisualCit interface for pipeline sketching
Text classification techniques, as well as a fused representation of textual, image, and metadata,
are areas of interest, although so far they have been inspected only preliminarily. The primary
goal would be to track known attributes of a situation, such as phases or topics, in order
to characterize it. Since in this case the inputs are formulated in natural language, special
6
http://visualcit.polimi.it:7778/
�attention is being paid to multilingualism. Multilingualism can be supported with the use and
the development of language-independent tools and representations, such as language-agnostic
embeddings, or approaches that support a relevant set of languages.
Characterization of social media posts can be almost naturally attached to a temporal dimen-
sion. As a first approximation, since the extracted information augments the stream of published
media, it is reasonable to assume that the information relates to near-realtime circumstances.
A more pressing issue is the one about the spatial dimension of the contents. If the contents
are not natively geolocated, as it usually happens7 , the location of the posts should be inferred.
This is usually a harder problem and faceted, since the location of the author could differ
from the location(s) related to the contents, or the hints for understanding the location could
be flimsy. A comprehensive approach to social media geolocation has been proposed in the
CIME system [14], which is currently being used by VisualCit. CIME disambiguates candidate
locations using Nominatim8 . The disambiguation algorithm considers candidate locations, their
distances, and their ranks in the OSM administrative hierarchy. CIME was exploited in the E2mC
project [12] to support rapid mapping activities within Copernicus Emergency Management
Services. Extracted geolocations can be further filtered with known geometries (e.g., countries
being monitored) and/or density-based approaches, also leveraging in-post and between-post
relationships.
3.3. Building and enhancing pipelines
Comprehensive and quick reaction to large scale events requires a systematic approach to
processing. Functionalities implemented so far are available with a service-based paradigm.
Composite functionalities can be instantiated as a processing pipeline, using a straightforward
configuration file, indicating components to be invoked and respective parameters, such as
confidence thresholds. Of course, as it happens for the events themselves, the configuration
of a pipeline is not always predictable. Data distributions, quality constraints and workforce
availability are likely to change over time. With this fact in mind, we evaluated an iterative
approach to provide suggestions to designers of data preprocessing pipelines. Iterations with
user feedback are meant to rapidly achieve the desired goals, reflecting application needs and
limitations and understanding the quality of the output. The designer is provided with an
informative environment in which components, constraints, and parameters can be evaluated.
Components typically consist of data filtering or augmentation modules, representing imple-
mentations of the tasks introduced in 3.2. Constraints are usually expressed on processing
quality, efficiency and cost. Figure 3 shows an example dashboard in which the effect of the
pipeline components on the final output is visualized interactively. The effect is measured in
terms of precision, recall and number of filtered items. The evaluation is performed using a
validated sample dataset, provided during the design process.
Processing posts with tailored pipelines dramatically reduces the number of posts. Depending
on application needs, the number of items that are filtered out and the quality of the output can
be balanced. Suggestions about possible improvements are provided, such as modifications to
the configuration of the pipeline, based on historical or current evidence. Suggestions could
7
For example, for privacy issues.
8
https://nominatim.openstreetmap.org/
� 1.0 1.0 1.0
0.8 0.8 0.8
0.6 0.6 0.6
0.4 0.4 0.4
precision precision precision
0.2 recall 0.2 recall 0.2 recall
reduction rate reduction rate reduction rate
0.00.0 0.2 0.4 0.6 0.8 1.0 0.00.0 0.2 0.4 0.6 0.8 1.0 0.00.0 0.2 0.4 0.6 0.8 1.0
Confidence threshold Confidence threshold Confidence threshold
(a) Is this a photo? (b) Are there two or more (c) Is this a public place?
persons?
Figure 3: Precision, recall and reduction rate responses to confidence threshold, on an example pipeline
also concern the execution order of the pipeline, for performance optimization. We showed
that, in some test cases, an optimized configuration could lead to around 33% of the original
execution time, without affecting the output.
4. Case studies
As case studies, we exploited, extended and adapted available tools to assess their reliability
when used as a triggering and analysis system for large scale disaster events. We applied
a combination of the proposed methodologies to two flood events that occurred in 2021 in
Thailand (September) and Nepal (June and July). The two events were selected since the United
Nations Satellite Centre (UNOSAT) supported both activations in Nepal and Thailand with
satellite-derived maps and AI-based flood detection analysis. In both case studies, we used
Twitter as a data source.
Initial evidence suggested the need for language-specific approaches rather than focusing on
contents written in English. Regarding event detection, the experimentation was started with a
limited amount of words. The word count signal over time proved to be sufficient to detect the
two events within a reasonable time frame. Using small dictionaries is less sensitive to noise
but poses a number of limitations, which have been discussed in subsection 3.1. A pipeline
consisting of the following tasks has been run in order to select relevant data:
1. Remove duplicated images and similar images.
2. Remove non-photos, such as drawings, screenshots and computer-generated images.
3. Remove not-safe-for-work images9 .
4. Geolocate posts using CIME.
Processing the posts with such a pipeline reduced the amount of data by orders of magnitude.
The output, aggregated by administrative region, was compared to official impact estimates
(Fig. 4 and 5) shows promising yet improvable results. We were able to process and geolocate
images at a rate of roughly 10,000 items per hour, on a single server machine. This proved to be
sufficient for real-time event monitoring.
9
“Not suitable for viewing at most places of employment”, according to the Merriam-Webster online dictionary.
� (a) Removed items (b) Kept items
Figure 4: Example images for (a) items being removed by the pipeline, and (b) items being kept
(a) Geolocations / inhabitants ratio by (b) Affected persons by region at
region, normalized by population September, 28th (source: ReliefWeb)
Figure 5: Comparing pipeline output and preliminary impact assessment, Thailand case study [13]
5. Future work
The current work in progress is aimed at detection methods, components for data extraction,
and process design support. Together, these elements can be leveraged to project the streams of
raw data onto spatio-temporal coordinates, in order to get a suitable and actionable description
of reality. So far, official, sensor and forecast data have been used as a reference for evaluating
the quality of the approaches. Also, they could be fused together, in order to build a multi-
faceted view of reality. This is the guiding goal for future work on the topic. At the same
time, since social media contents are highly multi-modal by definition, we want to explore how
different dimensions and aggregations of social media data can be combined in multi-modal
representations in order to better fit the required functionalities, such as classification tasks.
Acknowledgements This work was funded by the EU H2020 project Crowd4SDG “Citizen
Science for Monitoring Climate Impacts and Achieving Climate Resilience”, #872944.
References
[1] M. Imran, F. Ofli, D. Caragea, A. Torralba, Using AI and social media multimodal content
for disaster response and management: Opportunities, challenges, and future directions,
� Information Processing & Management 57 (2020) 102261.
[2] V. Lorini, C. Castillo, S. Peterson, P. Rufolo, H. Purohit, D. Pajarito, J. P. de Albuquerque,
C. Buntain, Social media for emergency management: Opportunities and challenges at the
intersection of research and practice, in: 18th International Conference on Information
Systems for Crisis Response and Management, 2021, pp. 772–777.
[3] T. Sakaki, M. Okazaki, Y. Matsuo, Tweet analysis for real-time event detection and
earthquake reporting system development, IEEE TKDE 25 (2013) 919–931.
[4] J. Fohringer, D. Dransch, H. Kreibich, K. Schröter, Social media as an information source
for rapid flood inundation mapping, Natural Hazards and Earth System Sciences 15 (2015)
2725–2738.
[5] K. Shoyama, Q. Cui, M. Hanashimaa, H. Sano, Y. Usuda, Emergency flood detection using
multiple information sources: Integrated analysis of natural hazard monitoring and social
media data, Science of the Total Environment 767 (2021) 1–11.
[6] D. T. Nguyen, F. Alam, F. Ofli, M. Imran, Automatic image filtering on social networks
using deep learning and perceptual hashing during crises, in: Proc. 14th ISCRAM Conf. –
Albi, France, 2017.
[7] V. Negri, D. Scuratti, S. Agresti, D. Rooein, G. Scalia, A. R. Shankar, J. L. Fernandez-Marquez,
M. J. Carman, B. Pernici, Image-based social sensing: Combining AI and the crowd to
mine policy-adherence indicators from twitter, in: 43rd IEEE/ACM Intl. Conf. on Sw. Eng.
Track Software Engineering in Society, ICSE (SEIS), Madrid, Spain, IEEE, 2021, pp. 92–101.
[8] A. Olteanu, C. Castillo, F. Diaz, S. Vieweg, Crisislex: A lexicon for collecting and filtering
microblogged communications in crises, Proceedings of the International AAAI Conference
on Web and Social Media 8 (2014) 376–385.
[9] A. Autelitano, B. Pernici, G. Scalia, Spatio-temporal mining of keywords for social media
cross-social crawling of emergency events, GeoInformatica 23 (2019) 425–447.
[10] B. Stollberg, T. De Groeve, The use of social media within the global disaster alert and
coordination system (GDACS), in: Proceedings of the 21st International Conference on
World Wide Web, 2012, pp. 703–706.
[11] V. Lorini, C. Castillo, D. Nappo, F. Dottori, P. Salamon, Social media alerts can improve,
but not replace hydrological models for forecasting floods, in: 2020 IEEE/WIC/ACM Intl.
Joint Conf. on Web Intelligence and Intelligent Agent Technology (WI-IAT), IEEE, 2020,
pp. 351–356.
[12] C. Havas, B. Resch, C. Francalanci, B. Pernici, G. Scalia, J. L. Fernandez-Marquez,
T. Van Achte, G. Zeug, M. R. R. Mondardini, D. Grandoni, et al., E2mC: Improving
emergency management service practice through social media and crowdsourcing analysis
in near real time, Sensors 17 (2017) 2766.
[13] C. Bono, B. Pernici, J. L. Fernandez-Marquez, A. R. Shankar, M. O. Mülâyim, E. Nemni,
Triggercit: Early flood alerting using twitter and geolocation–a comparison with alternative
sources, arXiv preprint arXiv:2202.12014, accepted for presentation at ISCRAM’22 (2022).
[14] G. Scalia, C. Francalanci, B. Pernici, Cime: Context-aware geolocation of emergency-related
posts, GeoInformatica (2021) 1–33.
�