1,752 research outputs found
A Dynamical Model of Twitter Activity Profiles
The advent of the era of Big Data has allowed many researchers to dig into
various socio-technical systems, including social media platforms. In
particular, these systems have provided them with certain verifiable means to
look into certain aspects of human behavior. In this work, we are specifically
interested in the behavior of individuals on social media platforms---how they
handle the information they get, and how they share it. We look into Twitter to
understand the dynamics behind the users' posting activities---tweets and
retweets---zooming in on topics that peaked in popularity. Three mechanisms are
considered: endogenous stimuli, exogenous stimuli, and a mechanism that
dictates the decay of interest of the population in a topic. We propose a model
involving two parameters and describing the tweeting
behaviour of users, which allow us to reconstruct the findings of Lehmann et
al. (2012) on the temporal profiles of popular Twitter hashtags. With this
model, we are able to accurately reproduce the temporal profile of user
engagements on Twitter. Furthermore, we introduce an alternative in classifying
the collective activities on the socio-technical system based on the model.Comment: 10 pages, 5 figure
Efficiency of Human Activity on Information Spreading on Twitter
Understanding the collective reaction to individual actions is key to
effectively spread information in social media. In this work we define
efficiency on Twitter, as the ratio between the emergent spreading process and
the activity employed by the user. We characterize this property by means of a
quantitative analysis of the structural and dynamical patterns emergent from
human interactions, and show it to be universal across several Twitter
conversations. We found that some influential users efficiently cause
remarkable collective reactions by each message sent, while the majority of
users must employ extremely larger efforts to reach similar effects. Next we
propose a model that reproduces the retweet cascades occurring on Twitter to
explain the emergent distribution of the user efficiency. The model shows that
the dynamical patterns of the conversations are strongly conditioned by the
topology of the underlying network. We conclude that the appearance of a small
fraction of extremely efficient users results from the heterogeneity of the
followers network and independently of the individual user behavior.Comment: 29 pages, 10 figure
Influence of augmented humans in online interactions during voting events
The advent of the digital era provided a fertile ground for the development
of virtual societies, complex systems influencing real-world dynamics.
Understanding online human behavior and its relevance beyond the digital
boundaries is still an open challenge. Here we show that online social
interactions during a massive voting event can be used to build an accurate map
of real-world political parties and electoral ranks. We provide evidence that
information flow and collective attention are often driven by a special class
of highly influential users, that we name "augmented humans", who exploit
thousands of automated agents, also known as bots, for enhancing their online
influence. We show that augmented humans generate deep information cascades, to
the same extent of news media and other broadcasters, while they uniformly
infiltrate across the full range of identified groups. Digital augmentation
represents the cyber-physical counterpart of the human desire to acquire power
within social systems.Comment: 11 page
On Web User Tracking: How Third-Party Http Requests Track Users' Browsing Patterns for Personalised Advertising
On today's Web, users trade access to their private data for content and
services. Advertising sustains the business model of many websites and
applications. Efficient and successful advertising relies on predicting users'
actions and tastes to suggest a range of products to buy. It follows that,
while surfing the Web users leave traces regarding their identity in the form
of activity patterns and unstructured data. We analyse how advertising networks
build user footprints and how the suggested advertising reacts to changes in
the user behaviour.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0653
Characterizing interactions in online social networks during exceptional events
Nowadays, millions of people interact on a daily basis on online social media
like Facebook and Twitter, where they share and discuss information about a
wide variety of topics. In this paper, we focus on a specific online social
network, Twitter, and we analyze multiple datasets each one consisting of
individuals' online activity before, during and after an exceptional event in
terms of volume of the communications registered. We consider important events
that occurred in different arenas that range from policy to culture or science.
For each dataset, the users' online activities are modeled by a multilayer
network in which each layer conveys a different kind of interaction,
specifically: retweeting, mentioning and replying. This representation allows
us to unveil that these distinct types of interaction produce networks with
different statistical properties, in particular concerning the degree
distribution and the clustering structure. These results suggests that models
of online activity cannot discard the information carried by this multilayer
representation of the system, and should account for the different processes
generated by the different kinds of interactions. Secondly, our analysis
unveils the presence of statistical regularities among the different events,
suggesting that the non-trivial topological patterns that we observe may
represent universal features of the social dynamics on online social networks
during exceptional events
A Universal Lifetime Distribution for Multi-Species Systems
Lifetime distributions of social entities, such as enterprises, products, and
media contents, are one of the fundamental statistics characterizing the social
dynamics. To investigate the lifetime distribution of mutually interacting
systems, simple models having a rule for additions and deletions of entities
are investigated. We found a quite universal lifetime distribution for various
kinds of inter-entity interactions, and it is well fitted by a
stretched-exponential function with an exponent close to 1/2. We propose a
"modified Red-Queen" hypothesis to explain this distribution. We also review
empirical studies on the lifetime distribution of social entities, and
discussed the applicability of the model.Comment: 10 pages, 6 figures, Proceedings of Social Modeling and Simulations +
Econophysics Colloquium 201
When-To-Post on Social Networks
For many users on social networks, one of the goals when broadcasting content
is to reach a large audience. The probability of receiving reactions to a
message differs for each user and depends on various factors, such as location,
daily and weekly behavior patterns and the visibility of the message. While
previous work has focused on overall network dynamics and message flow
cascades, the problem of recommending personalized posting times has remained
an underexplored topic of research. In this study, we formulate a when-to-post
problem, where the objective is to find the best times for a user to post on
social networks in order to maximize the probability of audience responses. To
understand the complexity of the problem, we examine user behavior in terms of
post-to-reaction times, and compare cross-network and cross-city weekly
reaction behavior for users in different cities, on both Twitter and Facebook.
We perform this analysis on over a billion posted messages and observed
reactions, and propose multiple approaches for generating personalized posting
schedules. We empirically assess these schedules on a sampled user set of 0.5
million active users and more than 25 million messages observed over a 56 day
period. We show that users see a reaction gain of up to 17% on Facebook and 4%
on Twitter when the recommended posting times are used. We open the dataset
used in this study, which includes timestamps for over 144 million posts and
over 1.1 billion reactions. The personalized schedules derived here are used in
a fully deployed production system to recommend posting times for millions of
users every day.Comment: 10 pages, to appear in KDD201
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