310 research outputs found
The call of the crowd: Event participation in location-based social services
Understanding the social and behavioral forces behind event participation is
not only interesting from the viewpoint of social science, but also has
important applications in the design of personalized event recommender systems.
This paper takes advantage of data from a widely used location-based social
network, Foursquare, to analyze event patterns in three metropolitan cities. We
put forward several hypotheses on the motivating factors of user participation
and confirm that social aspects play a major role in determining the likelihood
of a user to participate in an event. While an explicit social filtering signal
accounting for whether friends are attending dominates the factors, the
popularity of an event proves to also be a strong attractor. Further, we
capture an implicit social signal by performing random walks in a high
dimensional graph that encodes the place type preferences of friends and that
proves especially suited to identify relevant niche events for users. Our
findings on the extent to which the various temporal, spatial and social
aspects underlie users' event preferences lead us to further hypothesize that a
combination of factors better models users' event interests. We verify this
through a supervised learning framework. We show that for one in three users in
London and one in five users in New York and Chicago it identifies the exact
event the user would attend among the pool of suggestions.We acknowledge the support of Microsoft Research and EPSRC
through grant GALE (EP/K019392).This is the final published version. It's also available from AAAI at http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8068. Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved
Digital social interactions in the city: reflecting on location-based social media
In this paper we discuss how digital interactions are increasingly interwoven with spaces and places in urban settings and how such interactions are mediated by and in turn shape the technologies that facilitate them. We will focus on the understanding of interactions using location based social media (particularly Foursquare) as a way to reflect on issues of technological support to human activities, and on the relationship between code, digital agency and the physical world
Three Essays on Friend Recommendation Systems for Online Social Networks
Social networking sites (SNSs) first appeared in the mid-90s. In recent years, however, Web 2.0 technologies have made modern SNSs increasingly popular and easier to use, and social networking has expanded explosively across the web. This brought a massive number of new users. Two of the most popular SNSs, Facebook and Twitter, have reached one billion users and exceeded half billion users, respectively.
Too many new users may cause the cold start problem. Users sign up on a SNS and discover they do not have any friends. Normally, SNSs solve this problem by recommending potential friends. The current major methods for friend recommendations are profile matching and “friends-of-friends.” The profile matching method compares two users’ profiles. This is relatively inflexible because it ignores the changing nature of users. It also requires complete profiles. The friends-of-friends method can only find people who are likely to be previously known to each other and neglects many users who share the same interests. To the best of my knowledge, existing research has not proposed guidelines for building a better recommendation system based on context information (location information) and user-generated content (UGC).
This dissertation consists of three essays. The first essay focuses on location information and then develops a framework for using location to recommend friends--a framework that is not limited to making only known people recommendations but that also adds stranger recommendations. The second essay employs UGC by developing a text analytic framework that discovers users’ interests and personalities and uses this information to recommend friends. The third essay discusses friend recommendations in a certain type of online community – health and fitness social networking sites, physical activities and health status become more important factors in this case.
Essay 1: Location-sensitive Friend Recommendations in Online Social Networks
GPS-embedded smart devices and wearable devices such as smart phones, tablets, smart watches, etc., have significantly increased in recent years. Because of them, users can record their location at anytime and anyplace. SNSs such as Foursquare, Facebook, and Twitter all have developed their own location-based services to collect users’ location check-in data and provide location-sensitive services such as location-based promotions. None of these sites, however, have used location information to make friend recommendations.
In this essay, we investigate a new model to make friend recommendations. This model includes location check-in data as predictors and calculates users’ check-in histories--users’ life patterns--to make friend recommendations. The results of our experiment show that this novel model provides better performance in making friend recommendations.
Essay 2: Novel Friend Recommendations Based on User-generated Contents
More and more users have joined and contributed to SNSs. Users share stories of their daily life (such as having delicious food, enjoying shopping, traveling, hanging out, etc.) and leave comments. This huge amount of UGC could provide rich data for building an accurate, adaptable, effective, and extensible user model that reflects users’ interests, their sentiments about different type of locations, and their personalities. From the computer-supported social matching process, these attributes could influence friend matches. Unfortunately, none of the previous studies in this area have focused on using these extracted meta-text features for friend recommendation systems.
In this study, we develop a text analytic framework and apply it to UGCs on SNSs. By extracting interests and personality features from UGCs, we can make text-based friend recommendations. The results of our experiment show that text features could further improve recommendation performance.
Essay 3: Friend Recommendations in Health/Fitness Social Networking Sites
Thanks to the growing number of wearable devices, online health/fitness communities are becoming more and more popular. This type of social networking sites offers individuals the opportunity to monitor their diet process and motivating them to change their lifestyles. Users can improve their physical activity level and health status by receiving information, advice and supports from their friends in the social networks. Many studies have confirmed that social network structure and the degree of homophily in a network will affect how health behavior and innovations are spread. However, very few studies have focused on the opposite, the impact from users’ daily activities for building friendships in a health/fitness social networking site.
In this study, we track and collect users’ daily activities from Record, a famous online fitness social networking sites. By building an analytic framework, we test and evaluate how people’s daily activities could help friend recommendations. The results of our experiment have shown that by using the helps from these information, friend recommendation systems become more accurate and more precise
Language in Our Time: An Empirical Analysis of Hashtags
Hashtags in online social networks have gained tremendous popularity during
the past five years. The resulting large quantity of data has provided a new
lens into modern society. Previously, researchers mainly rely on data collected
from Twitter to study either a certain type of hashtags or a certain property
of hashtags. In this paper, we perform the first large-scale empirical analysis
of hashtags shared on Instagram, the major platform for hashtag-sharing. We
study hashtags from three different dimensions including the temporal-spatial
dimension, the semantic dimension, and the social dimension. Extensive
experiments performed on three large-scale datasets with more than 7 million
hashtags in total provide a series of interesting observations. First, we show
that the temporal patterns of hashtags can be categorized into four different
clusters, and people tend to share fewer hashtags at certain places and more
hashtags at others. Second, we observe that a non-negligible proportion of
hashtags exhibit large semantic displacement. We demonstrate hashtags that are
more uniformly shared among users, as quantified by the proposed hashtag
entropy, are less prone to semantic displacement. In the end, we propose a
bipartite graph embedding model to summarize users' hashtag profiles, and rely
on these profiles to perform friendship prediction. Evaluation results show
that our approach achieves an effective prediction with AUC (area under the ROC
curve) above 0.8 which demonstrates the strong social signals possessed in
hashtags.Comment: WWW 201
On the Accuracy of Hyper-local Geotagging of Social Media Content
Social media users share billions of items per year, only a small fraction of
which is geotagged. We present a data- driven approach for identifying
non-geotagged content items that can be associated with a hyper-local
geographic area by modeling the location distributions of hyper-local n-grams
that appear in the text. We explore the trade-off between accuracy, precision
and coverage of this method. Further, we explore differences across content
received from multiple platforms and devices, and show, for example, that
content shared via different sources and applications produces significantly
different geographic distributions, and that it is best to model and predict
location for items according to their source. Our findings show the potential
and the bounds of a data-driven approach to geotag short social media texts,
and offer implications for all applications that use data-driven approaches to
locate content.Comment: 10 page
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