109 research outputs found
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review
The task of multimedia geolocation is becoming an increasingly essential
component of the digital forensics toolkit to effectively combat human
trafficking, child sexual exploitation, and other illegal acts. Typically,
metadata-based geolocation information is stripped when multimedia content is
shared via instant messaging and social media. The intricacy of geolocating,
geotagging, or finding geographical clues in this content is often overly
burdensome for investigators. Recent research has shown that contemporary
advancements in artificial intelligence, specifically computer vision and deep
learning, show significant promise towards expediting the multimedia
geolocation task. This systematic literature review thoroughly examines the
state-of-the-art leveraging computer vision techniques for multimedia
geolocation and assesses their potential to expedite human trafficking
investigation. This includes a comprehensive overview of the application of
computer vision-based approaches to multimedia geolocation, identifies their
applicability in combating human trafficking, and highlights the potential
implications of enhanced multimedia geolocation for prosecuting human
trafficking. 123 articles inform this systematic literature review. The
findings suggest numerous potential paths for future impactful research on the
subject
Firsthand Opiates Abuse on Social Media: Monitoring Geospatial Patterns of Interest Through a Digital Cohort
In the last decade drug overdose deaths reached staggering proportions in the
US. Besides the raw yearly deaths count that is worrisome per se, an alarming
picture comes from the steep acceleration of such rate that increased by 21%
from 2015 to 2016. While traditional public health surveillance suffers from
its own biases and limitations, digital epidemiology offers a new lens to
extract signals from Web and Social Media that might be complementary to
official statistics. In this paper we present a computational approach to
identify a digital cohort that might provide an updated and complementary view
on the opioid crisis. We introduce an information retrieval algorithm suitable
to identify relevant subspaces of discussion on social media, for mining data
from users showing explicit interest in discussions about opioid consumption in
Reddit. Moreover, despite the pseudonymous nature of the user base, almost 1.5
million users were geolocated at the US state level, resembling the census
population distribution with a good agreement. A measure of prevalence of
interest in opiate consumption has been estimated at the state level, producing
a novel indicator with information that is not entirely encoded in the standard
surveillance. Finally, we further provide a domain specific vocabulary
containing informal lexicon and street nomenclature extracted by user-generated
content that can be used by researchers and practitioners to implement novel
digital public health surveillance methodologies for supporting policy makers
in fighting the opioid epidemic.Comment: Proceedings of the 2019 World Wide Web Conference (WWW '19
Balancing diversity to counter-measure geographical centralization in microblogging platforms
We study whether geographical centralization is reflected in the virtual population of microblogging platforms. A consequence of centralization is the decreased visibility and findability of content from less central locations. We propose to counteract geographical centralization in microblogging timelines by promoting geographical diversity through: 1) a characterization of imbalance in location interaction centralization over a graph of geographical interactions from user generated content; 2) geolocation of microposts using imbalance-aware content features in text classifiers, and evaluation of those classifiers according to their diversity and accuracy; 3) definition of a two-step information filtering algorithm to ensure diversity in summary timelines of events. We study our proposal through an analysis of a datase
Digital neighborhoods
With the advent of ‘big data’ there is an increased interest in using social media to describe city dynamics. This paper employs geo-located social media data to identify ‘digital neighborhoods’ – those areas in the city where social media is used more often. Starting with geo-located Twitter and Foursquare data for the New York City region in 2014, we applied spatial clustering techniques to detect significant groupings or ‘neighborhoods’ where social media use is high or low. The results show that beyond the business districts, digital neighborhoods occur in communities undergoing shifting socio-demographics. Neighborhoods that are not digitally oriented tend to have higher proportion of minorities and lower incomes, highlighting a social–economic divide in how social media is used in the city. Understanding the differences in these neighborhoods can help city planners interested in generating economic development proposals, civic engagement strategies, and urban design ideas that target these areas
Rapid opioid overdose response system technologies
Purpose of review Opioid overdose events are a time sensitive medical emergency, which is often reversible with naloxone administration if detected in time. Many countries are facing rising opioid overdose deaths and have been implementing rapid opioid overdose response Systems (ROORS). We describe how technology is increasingly being used in ROORS design, implementation and delivery. Recent findings Technology can contribute in significant ways to ROORS design, implementation, and delivery. Artificial intelligence-based modelling and simulations alongside wastewater-based epidemiology can be used to inform policy decisions around naloxone access laws and effective naloxone distribution strategies. Data linkage and machine learning projects can support service delivery organizations to mobilize and distribute community resources in support of ROORS. Digital phenotyping is an advancement in data linkage and machine learning projects, potentially leading to precision overdose responses. At the coalface, opioid overdose detection devices through fixed location or wearable sensors, improved connectivity, smartphone applications and drone-based emergency naloxone delivery all have a role in improving outcomes from opioid overdose. Data driven technologies also have an important role in empowering community responses to opioid overdose. Summary This review highlights the importance of technology applied to every aspect of ROORS. Key areas of development include the need to protect marginalized groups from algorithmic bias, a better understanding of individual overdose trajectories and new reversal agents and improved drug delivery methods.PostprintPeer reviewe
SOCIAL MEDIA FOOTPRINTS OF PUBLIC PERCEPTION ON ENERGY ISSUES IN THE CONTERMINOUS UNITED STATES
Energy has been at the top of the national and global political agenda along with other concomitant challenges, such as poverty, disaster and climate change. Social perception on various energy issues, such as its availability, development and consumption deeply affect our energy future. This type of information is traditionally collected through structured energy surveys. However, these surveys are often subject to formidable costs and intensive labor, as well as a lack of temporal dimensions. Social media can provide a more cost-effective solution to collect massive amount of data on public opinions in a timely manner that may complement the survey. The purpose of this study is to use machine learning algorithms and social media conversations to characterize the spatiotemporal topics and social perception on different energy in terms of spatial and temporal dimensions. Text analysis algorithms, such as sentiment analysis and topic analysis, were employed to offer insights into the public attitudes and those prominent issues related to energy. The results show that the energy related public perceptions exhibited spatiotemporal dynamics. The study is expected to help inform decision making, formulate national energy policies, and update entrepreneurial energy development decisions
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