236 research outputs found
Precursors and Laggards: An Analysis of Semantic Temporal Relationships on a Blog Network
We explore the hypothesis that it is possible to obtain information about the
dynamics of a blog network by analysing the temporal relationships between
blogs at a semantic level, and that this type of analysis adds to the knowledge
that can be extracted by studying the network only at the structural level of
URL links. We present an algorithm to automatically detect fine-grained
discussion topics, characterized by n-grams and time intervals. We then propose
a probabilistic model to estimate the temporal relationships that blogs have
with one another. We define the precursor score of blog A in relation to blog B
as the probability that A enters a new topic before B, discounting the effect
created by asymmetric posting rates. Network-level metrics of precursor and
laggard behavior are derived from these dyadic precursor score estimations.
This model is used to analyze a network of French political blogs. The scores
are compared to traditional link degree metrics. We obtain insights into the
dynamics of topic participation on this network, as well as the relationship
between precursor/laggard and linking behaviors. We validate and analyze
results with the help of an expert on the French blogosphere. Finally, we
propose possible applications to the improvement of search engine ranking
algorithms
Validating Network Value of Influencers by means of Explanations
Recently, there has been significant interest in social influence analysis.
One of the central problems in this area is the problem of identifying
influencers, such that by convincing these users to perform a certain action
(like buying a new product), a large number of other users get influenced to
follow the action. The client of such an application is a marketer who would
target these influencers for marketing a given new product, say by providing
free samples or discounts. It is natural that before committing resources for
targeting an influencer the marketer would be interested in validating the
influence (or network value) of influencers returned. This requires digging
deeper into such analytical questions as: who are their followers, on what
actions (or products) they are influential, etc. However, the current
approaches to identifying influencers largely work as a black box in this
respect. The goal of this paper is to open up the black box, address these
questions and provide informative and crisp explanations for validating the
network value of influencers.
We formulate the problem of providing explanations (called PROXI) as a
discrete optimization problem of feature selection. We show that PROXI is not
only NP-hard to solve exactly, it is NP-hard to approximate within any
reasonable factor. Nevertheless, we show interesting properties of the
objective function and develop an intuitive greedy heuristic. We perform
detailed experimental analysis on two real world datasets - Twitter and
Flixster, and show that our approach is useful in generating concise and
insightful explanations of the influence distribution of users and that our
greedy algorithm is effective and efficient with respect to several baselines
The semantic drift of quotations in blogspace: a case study in short-term cultural evolution
First revision (major) for Cognitive ScienceWe present an empirical case study which connects psycholinguistics with the field of cultural evolution, in order to test for the existence of cultural attractors in the evolution of quotations. Such attractors have been proposed as a useful concept for understanding cultural evolution in relation with individual cognition, but their existence has been hard to test. We focus on the transformation of quotations when they are copied from blog to blog or media website: by coding words with a number of well-studied lexical features, we show that the way words are substituted in quotations is consistent (1) with the hypothesis of cultural attractors, and (2) with known effects of the word features. In particular, words known to be harder to recall in lists have a higher tendency to be substituted, and words easier to recall are produced instead. Our results support the hypothesis that cultural attractors can result from the combination of individual cognitive biases in the interpretation and reproduction of representations
Cultural Diffusion and Trends in Facebook Photographs
Online social media is a social vehicle in which people share various moments
of their lives with their friends, such as playing sports, cooking dinner or
just taking a selfie for fun, via visual means, that is, photographs. Our study
takes a closer look at the popular visual concepts illustrating various
cultural lifestyles from aggregated, de-identified photographs. We perform
analysis both at macroscopic and microscopic levels, to gain novel insights
about global and local visual trends as well as the dynamics of interpersonal
cultural exchange and diffusion among Facebook friends. We processed images by
automatically classifying the visual content by a convolutional neural network
(CNN). Through various statistical tests, we find that socially tied
individuals more likely post images showing similar cultural lifestyles. To
further identify the main cause of the observed social correlation, we use the
Shuffle test and the Preference-based Matched Estimation (PME) test to
distinguish the effects of influence and homophily. The results indicate that
the visual content of each user's photographs are temporally, although not
necessarily causally, correlated with the photographs of their friends, which
may suggest the effect of influence. Our paper demonstrates that Facebook
photographs exhibit diverse cultural lifestyles and preferences and that the
social interaction mediated through the visual channel in social media can be
an effective mechanism for cultural diffusion.Comment: 10 pages, To appear in ICWSM 2017 (Full Paper
Tracing the Use of Practices through Networks of Collaboration
An active line of research has used on-line data to study the ways in which
discrete units of information---including messages, photos, product
recommendations, group invitations---spread through social networks. There is
relatively little understanding, however, of how on-line data might help in
studying the diffusion of more complex {\em practices}---roughly, routines or
styles of work that are generally handed down from one person to another
through collaboration or mentorship. In this work, we propose a framework
together with a novel type of data analysis that seeks to study the spread of
such practices by tracking their syntactic signatures in large document
collections. Central to this framework is the notion of an "inheritance graph"
that represents how people pass the practice on to others through
collaboration. Our analysis of these inheritance graphs demonstrates that we
can trace a significant number of practices over long time-spans, and we show
that the structure of these graphs can help in predicting the longevity of
collaborations within a field, as well as the fitness of the practices
themselves.Comment: To Appear in Proceedings of ICWSM 2017, data at
https://github.com/CornellNLP/Macro
Socio-semantic dynamics in a blog network
The blogosphere can be construed as a knowledge network made of bloggers who
are interacting through a social network to share, exchange or produce
information. We claim that the social and semantic dimensions are essentially
co-determined and propose to investigate the co-evolutionary dynamics of the
blogosphere by examining two intertwined issues: First, how does knowledge
distribution drive new interactions and thus influence the social network
topology? Second, which role structural network properties play in the
information circulation in the system? We adopt an empirical standpoint by
analyzing the semantic and social activity of a portion of the US political
blogosphere, monitored on a period of four months
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