EVENT REPORT

Report on the 11th International Workshop on
Location and the Web (LocWeb 2021) and the11th Temporal Web Analytics Workshop(TempWeb2021) at WWW2021
Dirk Ahlers
NTNU – Norwegian University of
Science and Technology
Trondheim, Norway
dirk.ahlers@ntnu.no
Marc Spaniol
University of Caen Normandy
Caen, France
marc.spaniol@unicaen.fr

Erik Wilde
Axway
ewilde@axway.com

Ricardo Baeza-Yates
Northeastern University
Silicon Valley, CA, USArbaeza@acm.org

Omar AlonsoInstacart
San Francisco, CA, USAoralonso@gmail.com
Abstract
LocWeb and TempWeb 2021 were the eleventh events in their workshop series and took placeco-located on 12th April 2021 in conjunction with The Web Conference WWW 2021. They
were intended to be held in Ljubljana, Slovenia as a potentially hybrid event, but due to thepandemic, were fully moved online.
LocWeb and TempWeb were held as one colocated session with a merged programme andshared topics to explore similarities and introduce attendees to the two related and complementary areas. LocWeb 2021 explored the intersection of location-based analytics and Web
architecture with a focus on on Web-scale services and location-aware information access.
TempWeb 2021 discussed temporal analytics at a Web scale with experts from science andindustry.
Date: 12 April, 2021.
Websites: https://dhere.de/locweb/locweb2021 and http://temporalweb.net/.

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1

Introduction

LocWeb and TempWeb 2021 were the eleventh events in their workshop series and took placeco-located on 12th April 2021 in conjunction with The Web Conference WWW 2021.1 They wereintended to be held in Ljubljana, Slovenia as a potentially hybrid event, but due to the pandemic,
were fully moved online. This is therefore the second time that LocWeb and TempWeb did nothave a physical location. Our experiences with that have already been discussed in the report for
LocWeb 2020.
LocWeb and TempWeb were held as one colocated session with a merged programme to explore
similarities and introduce attendees to the two related and complementary topics. Temporal andspatial analysis and retrieval share many similarities in how they are important enablers, but
are often derived, second class citizens, that need additional efforts to play to their strengths.
Combined spatio-temporal analytics is becoming more important as a method in Web mining.
This was a good way to share insights, while keeping the individual workshops’ characteristics.
The similarities allowed us to easily build the shared programme, which we think worked well,under four sessions: User Behavior, Networks & Graphs, Analytics & Visualization, Time & Space.
LocWeb explored the intersection of location-based analytics and Web architecture with afocus on on Web-scale services and systems facilitating location-aware information access as wellas on Spatial Social Computing on the Web. Workshop details, including most presentations,can be found on the workshop homepage2 and on the workshop series homepage.3 The previous
LocWeb report for 2020 has been published in SIGIR Forum [Ahlers et al., 2020] as well as thosefor previous years.
The focus of TempWeb and the topics addressed are a “natural” match with The Web Conference. With digital content born almost two decades ago, the need for a more systematic
exploitation of our digital cultural heritage as well as new analysis techniques, becomes evident.
Hence, TempWeb is an ideal venue to exchange knowledge about temporal analytics at a Web scalewith experts from science and industry. Further workshop details can be found on the workshop
homepage.4

2

LocWeb Workshop Theme and Topics

LocWeb continues the main theme of Web-scale Location-Aware Information Access and spatialsocial computing. Subtopics include (i) geospatial semantics, systems, and standards; (ii) largescale geospatial and geo-social ecosystems; (iii) mobility; (iv) location in the Web of Things; and(v) mining and searching geospatial data on the Web. The workshop encourages work describing
Web-mediated or Web-scale approaches that build on reliable foundations, and that thoroughly
understand and embrace the geospatial dimension through interdisciplinary perspectives.
The workshop’s topics of interest were: Location-Aware Information Access, Spatial Social Behavior, Location-Aware Web-Scale Systems and Services, Geospatial Data Science for Social Good,
Experience of virtual and physical place through online mediation, Physical and virtual/online
1

https://www2021.thewebconf.org/
https://dhere.de/locweb/locweb2021/
3
https://dhere.de/locweb/
4
http://temporalweb.net/
2

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spaces for collaboration and information access, Urban Planning and Citizen Engagement, Geospatial aspects of Smart Cities, Location in the Internet/Web of Things, Open Geospatial Web Data,
Location prediction in social media and the Web, Influence modeling and processing in staticand dynamic spatio-social graphs, Evaluation of frameworks, metrics and algorithms, Large-scale
Geospatial Ecosystems, Standards for Location and Mobility Data, Modeling Location and Location Interaction, Location-Based Social Networks, Geospatial Web Search and Mining, Mobile
Search and Location-Based Recommendation.

3

TempWeb Workshop Objectives and Topics

The objective of TempWeb is to provide a venue for researchers of all domains (IE/IR, Web mining,etc.) where the temporal dimension opens an entirely new range of possibilities and challenges.
The workshop’s ambition is to keep shaping a community of interest on research issues resulting
from the introduction of the time dimension in web mining and analysis. The maturity of the
Web as well as the emergence of large-scale repositories of web content makes this very timely andas a result, a growing number of research projects and services are emerging that have this focus
in common. Having a dedicated workshop will help, we believe, to take a rich and cross-domain
approach to this continuous research with a strong focus on the temporal dimension.
To this end, TempWeb focuses on investigating infrastructures, scalable methods, and innovative software for aggregating, querying, and analyzing heterogeneous data at Web scale. Emphasis
will be given to temporal data analysis along the time dimension for web data that has been collected over extended time periods. A major challenge in this regard is the sheer size of the data
it exposes and the ability to make sense of it in a useful and meaningful manner for its users. It
is worth noting that this trend of using big data to make inferences is not specific to web content
analytics. A now-common strategy in post-genomic biology is to measure, quantitatively, theaction of all (or as many as possible) of the genes at the level of the transcriptome, proteome,metabolome and phenotype, and to use computerized methods to infer gene function via various
kinds of pattern recognition techniques. On the Web, to a large extent, we have also reached thispoint. Web scale data analytics therefore needs to develop infrastructures and extended analytical
tools to make sense of these.

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LocWeb Workshop Contributions

The workshop had one keynote and 3 full papers, forming an interdisciplinary combination of
topics. Our PC members provided 3–4 reviews per paper on average, for a 60% acceptance
rate. The proceedings [Ahlers et al., 2021] are available in the ACM Digital Library5 as part ofthe overall WWW 2021 Companion. The papers are openly available when following the linkfrom the conference proceedings page.6 Slides are linked – where available – from the workshop
homepage.
In her keynote, Flora Salim talked about Learning Spatio-Temporal Behavioural Representations for Urban Activity Forecasting [Salim, 2021]. She presented work around human activity
5
6

https://dl.acm.org/doi/proceedings/10.1145/3442442#heading8
https://www2021.thewebconf.org/wp-content/uploads/2021/07/toc_companion.html

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patterns in cities for increased sustainability. She shared technical approaches around large-scale
mutivariate sensor data from heterogeneous sources – including raster and point data, and withquality issues – contextual modeling, and spatio-temporal predictions and recommendations, forexample trajectory prediction, visitor intent, and urban flow forecasting. She also talked about
spatial data in these cases being based on timeseries data, semantic categorisation, and spationtemporal graph embeddings, as well as the question of granularity of both space and time for bothdata and predictions.
In Cross-city Analysis of Location-based Sentiment in User-generated Text [Stelzmüller et al.,2021], Christopher Stelzmüller, Sebastian Tanzer and Markus Schedl presented their approach tounderstand how citizens live and feel based on geolocated user-generated content and how thatcan be used in urban planning. Their work is novel in that it aims to understand relations between
location and sentiment between cities around the world. That abstracts from individual sentiments
and may give a “sentiment baseline” as a sentiment timeseries per POI category worldwide. Theyuse POIs from the OpenStreetMap dataset and sentiment extracted from geolocated Tweets forthe aggregated analysis. As in other work, the results depend on the quality of the available
OSM data and the availability of Tweets about the respective POIs. They note that non-english
speaking cities show weaker relations between POI and sentiment, and also that there is a bias
making it more difficult to apply to developing countries, not only on spatial behaviour but also
on assumptions on POI use and other cases that would be valuable to follow up.
In Understanding & Predicting User Lifetime with Machine Learning in an Anonymous LocationBased Social Network [Reelfs et al., 2021], Jens Helge Reelfs, Oliver Hohlfeld, Max Bergmann and
Niklas Henckell use location analysis within the Jodel anonymous LBSN to predict user churn.
They use a data set of 3 years from Saudi Arabia, where they examine the disjoint local communities They could show similarities across communities, with larger cities being more similar, anda countrywide model prodiving easier and suitable results.
Ovidiu Dan, Vaibhav Parikh, and Brian D. Davison look at improving IP address geolocation
in IP Geolocation using Traceroute Location Propagation and IP Range Location Interpolation[Dan et al., 2021]. The authors are in a unique position as they can correlate 8.4 million IP
addresses with measured locations from Bing mobile searches. This allows them to confirm thatcommercial IP geolocation databases are often inaccurate or lack sufficient granularity for certainuse cases. It further allows them to have high granularity ground truth data available for theirnovel technique, which combines smart use of traceroute information with an interpolation of IP
locations. They show how to propagate physical location information along traceroute paths andinterpolate missing locations for certain IPs from the rest of their range, and also by the heuristicthat differences in latency time related to geographic proximity. The work can be used with the
CAIDA Archipelago (Ark) measurement infrastructure.

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TempWeb Workshop Contributions

For its eleventh edition, TempWeb accepted four very positively reviewed submissions for oralpresentation (acceptance rate of 50%). We interpret the high quality of the submissions and thefrequent contributors to TempWeb, as indicators of an evolving community. It shows a clear signof a positive dynamic in the study of time in the scope of the Web and evidence of the relevanceof this effort.
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The keynote talk was given by Prof. Ricardo Campos who is a Professor at Instituto Politécnicode Tomar in Portugal. Ricardo’s talk on “Telling Stories through Timelines” gave a perspectiveon timelines as a central piece for organizing events and complex topics. Ricardo’s talk coveredsummarization, construction of ground truth, evaluation and included demos that highlighted themain concepts.
The paper by Petrović, and Scholtes introduced “PaCo: Fast Counting of Causal Paths in
Temporal Network Data” [Petrović and Scholtes, 2021]. In their work the authors address the
problem, that network analysis cannot be directly applied to time-stamped network data. In
particular, this problem arises time series data capturing, e.g., time-stamped social interactions,time-resolved cooccurrences or other types of relational time series. For example, the time-stamped
links (A, B) and (B, C) the chronological ordering and timing determines whether a causal pathfrom node A via B to C exists. To this end, they presented an efficient algorithm to count causal
paths in time-stamped network data.
The second submission on “Learning to Persist: Exploring the Tradeoff Between Model Optimization and Experience Consistency” by Goldenberg et al. tackled issues in machine learningmodels and recommender systems in Web applications [Goldenberg et al., 2021]. To this end,
they analyzed the tradeoff between providing the user with a consistent experience and suggesting
an up-to-date optimal treatment. In order to overcome the challenges addressed, they proposeda hybrid approach utilizing a Persistence Key and Persistence Timeout. The proposed method
provided a robust engineering solution and can be further calibrated for the best hyperparameters.
In “Analysis and Visualisation of Time Series Data on Networks with Pathpy” by Hackl et al.
an open source software package was presented [Hackl et al., 2021]. The tool presented implements
statistical techniques to learn optimal graphical models for the causal topology generated bypaths in time-series data. In particular, pathpy supports network analysis and machine learningtechniques for temporal data on networks. As it is built on robust, scalable, and easy-to-use data
structures that are coherent with python’s data science stack, pathpy is suitable for a variety ofdata science tasks in the context of temporal Web data.
Finally, the paper “Visualisation of Temporal Network Data via Time-Aware Static Representations with HOTVis” by Perriy and Scholtes presented a graph visualization tool for the visual
analysis of temporal network data [Perri and Scholtes, 2021]. In particular, their software allows atime-aware static network visualizations that highlight the causal topology of temporal networks.
Thus, vertices that can directly and indirectly influence each other are highlighted in order tosupport dynamic graph visualizations.

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Conclusion and Future Directions

The discussion revolved around how time and space are often co-dependent aspects in much of thework presented here, and general experiences with the workshops. A recurring theme was data
quality and location granularity, as well as robust system development.
The co-located format of the workshops was a valuable experiment to bring different topics
and viewpoints together. Both workshops have been accepted for The Web Conference 2022.

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Acknowledgements
We thank the authors of submitted papers for their efforts, and all workshop attendees. We
also thank our LocWeb co-organisers Rossano Schifanella (University of Turin, Italy) and Jalal S.
Alowibdi (University of Jeddah, Saudi Arabia). Special acknowledgement go to the members of
the LocWeb program committee for their hard work in reviewing the contributions and providing
substantial feedback on a tight deadline:
Anastasios Noulas (New York University), Andreas Henrich (University of Bamberg), Arjen
de Vries (Radboud Universiteit), Auriol Degbelo (University of Münster), Bruno Martins (ISTand INESC-ID – Instituto Superior Técnico, University of Lisbon), Carsten Keßler (Aalborg
University Copenhagen), Christopher Jones (University of Cardiff), Clodoveu Davis (Universidade
Federal de Minas Gerais), Francisco J. Lopez-Pellicer (University of Zaragoza), Lisette Espı́nNoboa (GESIS – Leibniz-Institute for the Social Sciences, University of Koblenz-Landau), Luca
Aiello (IT University of Copenhagen), Rainer Simon (AIT – Austrian Institute of Technology),
Ross Purves (University of Zurich), Torsten Suel (New York University), Pieter Colpaert (Ghent
University), Yana Volkovich (Xandr).
TempWeb thanks its keynote speaker, Ricardo Campos (Instituto Politécnico de Tomar, Portugal); and its programme committee:
Eytan Adar (University of Michigan, USA), Ralitsa Angelova (Google, Switzerland), Srikanta
Bedathur (IIIT-Delhi, India), Andras A. Benczur (Hungarian Academy of Science, Hungary),
Klaus Berberich (Univ. of Applied Sciences, Germany), Ricardo Campos (Polytechnic Institute ofTomar, Portugal), Renata Galante (Univ. Federal do Rio Grande do Sul, Brazil), Adam Jatowt(Kyoto University, Japan), Nattiya Kanhabua (Upwork, Bangkok, Thailand), Scott Kirkpatrick(Hebrew University Jerusalem, Israel), Frank McCown (Harding University, USA), Michael Nelson (Old Dominion University, USA), Nikos Ntarmos (University of Glasgow, UK), Kjetil Nørvåg(Norwegian Univ. of Science and Tech., Norway), Thomas Risse (Univ. Johann Christian Senckenberg, Germany), Andreas Spitz (EPFL Lausanne, Switzerland), Jannik Strötgen (Bosch Center
for AI, Germany), Torsten Suel (NYU Polytechnic, USA), Masashi Toyoda (Tokyo University,
Japan), Gerhard Weikum (Max-Planck-Institut für Informatik, Germany).
We further would like to thank the organizers of WWW2021 for hosting the workshops andproviding the online meeting facilities.

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