40 research outputs found
Online Popularity and Topical Interests through the Lens of Instagram
Online socio-technical systems can be studied as proxy of the real world to
investigate human behavior and social interactions at scale. Here we focus on
Instagram, a media-sharing online platform whose popularity has been rising up
to gathering hundred millions users. Instagram exhibits a mixture of features
including social structure, social tagging and media sharing. The network of
social interactions among users models various dynamics including
follower/followee relations and users' communication by means of
posts/comments. Users can upload and tag media such as photos and pictures, and
they can "like" and comment each piece of information on the platform. In this
work we investigate three major aspects on our Instagram dataset: (i) the
structural characteristics of its network of heterogeneous interactions, to
unveil the emergence of self organization and topically-induced community
structure; (ii) the dynamics of content production and consumption, to
understand how global trends and popular users emerge; (iii) the behavior of
users labeling media with tags, to determine how they devote their attention
and to explore the variety of their topical interests. Our analysis provides
clues to understand human behavior dynamics on socio-technical systems,
specifically users and content popularity, the mechanisms of users'
interactions in online environments and how collective trends emerge from
individuals' topical interests.Comment: 11 pages, 11 figures, Proceedings of ACM Hypertext 201
How Image-Based Social Media Websites Support Social Movements
The Internet has disrupted the traditional progression of social movements. We explore common characteristics of image-based activism on Instagram by qualitatively analyzing 300 Instagram posts from three social movements: Black Lives Matter, the battle against defunding Planned Parenthood, and the backlash against the Indiana Religious Freedom Restoration Act. We found that common types of images emerged among the three social movements, indicating a possible underlying pattern in social movement content posted on Instagram. Users also engage in workarounds to leverage Instagram toward a collective goal, going beyond the features offered by the platform to communicate their message. These findings have implications for future work studying social movement theories online
MONITORING POTENTIAL DRUG INTERACTIONS AND REACTIONS VIA NETWORK ANALYSIS OF INSTAGRAM USER TIMELINES
Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that have not been hitherto possible. Given the large number of users, analysis of social media data may be useful to identify under-reported, population-level pathology associated with DDI, thus further contributing to improvements in population health. Moreover, tapping into this data allows us to infer drug interactions with natural products-including cannabis-which constitute an array of DDI very poorly explored by biomedical research thus far. Our goal is to determine the potential of Instagram for public health monitoring and surveillance for DDI, ADR, and behavioral pathology at large. Most social media analysis focuses on Twitter and Facebook, but Instagram is an increasingly important platform, especially among teens, with unrestricted access of public posts, high availability of posts with geolocation coordinates, and images to supplement textual analysis. Using drug, symptom, and natural product dictionaries for identification of the various types of DDI and ADR evidence, we have collected close to 7000 user timelines spanning from October 2010 to June 2015.We report on 1) the development of a monitoring tool to easily observe user-level timelines associated with drug and symptom terms of interest, and 2) population-level behavior via the analysis of co-occurrence networks computed from user timelines at three different scales: monthly, weekly, and daily occurrences. Analysis of these networks further reveals 3) drug and symptom direct and indirect associations with greater support in user timelines, as well as 4) clusters of symptoms and drugs revealed by the collective behavior of the observed population. This demonstrates that Instagram contains much drug- and pathology specific data for public health monitoring of DDI and ADR, and that complex network analysis provides an important toolbox to extract health-related associations and their support from large-scale social media data
A Random Growth Model with any Real or Theoretical Degree Distribution
The degree distributions of complex networks are usually considered to be
power law. However, it is not the case for a large number of them. We thus
propose a new model able to build random growing networks with (almost) any
wanted degree distribution. The degree distribution can either be theoretical
or extracted from a real-world network. The main idea is to invert the
recurrence equation commonly used to compute the degree distribution in order
to find a convenient attachment function for node connections - commonly chosen
as linear. We compute this attachment function for some classical
distributions, as the power-law, broken power-law, geometric and Poisson
distributions. We also use the model on an undirected version of the Twitter
network, for which the degree distribution has an unusual shape. We finally
show that the divergence of chosen attachment functions is heavily links to the
heavy-tailed property of the obtained degree distributions.Comment: 23 pages, 3 figure
Latent Space Model for Multi-Modal Social Data
With the emergence of social networking services, researchers enjoy the
increasing availability of large-scale heterogenous datasets capturing online
user interactions and behaviors. Traditional analysis of techno-social systems
data has focused mainly on describing either the dynamics of social
interactions, or the attributes and behaviors of the users. However,
overwhelming empirical evidence suggests that the two dimensions affect one
another, and therefore they should be jointly modeled and analyzed in a
multi-modal framework. The benefits of such an approach include the ability to
build better predictive models, leveraging social network information as well
as user behavioral signals. To this purpose, here we propose the Constrained
Latent Space Model (CLSM), a generalized framework that combines Mixed
Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA)
incorporating a constraint that forces the latent space to concurrently
describe the multiple data modalities. We derive an efficient inference
algorithm based on Variational Expectation Maximization that has a
computational cost linear in the size of the network, thus making it feasible
to analyze massive social datasets. We validate the proposed framework on two
problems: prediction of social interactions from user attributes and behaviors,
and behavior prediction exploiting network information. We perform experiments
with a variety of multi-modal social systems, spanning location-based social
networks (Gowalla), social media services (Instagram, Orkut), e-commerce and
review sites (Amazon, Ciao), and finally citation networks (Cora). The results
indicate significant improvement in prediction accuracy over state of the art
methods, and demonstrate the flexibility of the proposed approach for
addressing a variety of different learning problems commonly occurring with
multi-modal social data.Comment: 12 pages, 7 figures, 2 table
Performance Dynamics and Success in Online Games
Online data provide a way to monitor how users behave in social systems like
social networks and online games, and understand which features turn an
ordinary individual into a successful one. Here, we propose to study individual
performance and success in Multiplayer Online Battle Arena (MOBA) games. Our
purpose is to identify those behaviors and playing styles that are
characteristic of players with high skill level and that distinguish them from
other players. To this aim, we study Defense of the ancient 2 (Dota 2), a
popular MOBA game. Our findings highlight three main aspects to be successful
in the game: (i) players need to have a warm-up period to enhance their
performance in the game; (ii) having a long in-game experience does not
necessarily translate in achieving better skills; but rather, (iii) players
that reach high skill levels differentiate from others because of their
aggressive playing strategy, which implies to kill opponents more often than
cooperating with teammates, and trying to give an early end to the match
Hashtags on Instagram: Self-created or Mediated by Best Practices and Tools?
Social media enables conversations mediated through documents as texts, audio, images, or videos. Likewise, hashtags became an essential medium for social media communication. Instagram is well-known as one of the current platforms for hashtagging. This exploratory study investigates how hashtags used on Instagram became established in respect of self-creation and best practices or tools. The analysis is based on data obtained from an online survey (N = 1,006) of Instagram users. 55.7% of the respondents use hashtags on Instagram. Only self-created hashtags are assigned by 41.4%, whereas 58.6% are (sometimes) inspired by others. Best practices and tools based on friends/other users or Instagram functions are more frequently used in contrast to offers from influencers or third-parties (e.g. guides, hashtag-sets). Furthermore, the majority does not intentionally use false hashtags. This study enables a first overview of the Instagram users’ hashtagging creation behavior and selection process