241,406 research outputs found
A hierarchical Bayesian model for predicting ecological interactions using scaled evolutionary relationships
Identifying undocumented or potential future interactions among species is a
challenge facing modern ecologists. Recent link prediction methods rely on
trait data, however large species interaction databases are typically sparse
and covariates are limited to only a fraction of species. On the other hand,
evolutionary relationships, encoded as phylogenetic trees, can act as proxies
for underlying traits and historical patterns of parasite sharing among hosts.
We show that using a network-based conditional model, phylogenetic information
provides strong predictive power in a recently published global database of
host-parasite interactions. By scaling the phylogeny using an evolutionary
model, our method allows for biological interpretation often missing from
latent variable models. To further improve on the phylogeny-only model, we
combine a hierarchical Bayesian latent score framework for bipartite graphs
that accounts for the number of interactions per species with the host
dependence informed by phylogeny. Combining the two information sources yields
significant improvement in predictive accuracy over each of the submodels
alone. As many interaction networks are constructed from presence-only data, we
extend the model by integrating a correction mechanism for missing
interactions, which proves valuable in reducing uncertainty in unobserved
interactions.Comment: To appear in the Annals of Applied Statistic
Predictability in an unpredictable artificial cultural market
In social, economic and cultural situations in which the decisions of
individuals are influenced directly by the decisions of others, there appears
to be an inherently high level of ex ante unpredictability. In cultural markets
such as films, songs and books, well-informed experts routinely make
predictions which turn out to be incorrect.
We examine the extent to which the existence of social influence may,
somewhat paradoxically, increase the extent to which winners can be identified
at a very early stage in the process. Once the process of choice has begun,
only a very small number of decisions may be necessary to give a reasonable
prospect of being able to identify the eventual winner.
We illustrate this by an analysis of the music download experiments of
Salganik et.al. (2006). We derive a rule for early identification of the
eventual winner. Although not perfect, it gives considerable practical success.
We validate the rule by applying it to similar data not used in the process of
constructing the rule
What Twitter Profile and Posted Images Reveal About Depression and Anxiety
Previous work has found strong links between the choice of social media
images and users' emotions, demographics and personality traits. In this study,
we examine which attributes of profile and posted images are associated with
depression and anxiety of Twitter users. We used a sample of 28,749 Facebook
users to build a language prediction model of survey-reported depression and
anxiety, and validated it on Twitter on a sample of 887 users who had taken
anxiety and depression surveys. We then applied it to a different set of 4,132
Twitter users to impute language-based depression and anxiety labels, and
extracted interpretable features of posted and profile pictures to uncover the
associations with users' depression and anxiety, controlling for demographics.
For depression, we find that profile pictures suppress positive emotions rather
than display more negative emotions, likely because of social media
self-presentation biases. They also tend to show the single face of the user
(rather than show her in groups of friends), marking increased focus on the
self, emblematic for depression. Posted images are dominated by grayscale and
low aesthetic cohesion across a variety of image features. Profile images of
anxious users are similarly marked by grayscale and low aesthetic cohesion, but
less so than those of depressed users. Finally, we show that image features can
be used to predict depression and anxiety, and that multitask learning that
includes a joint modeling of demographics improves prediction performance.
Overall, we find that the image attributes that mark depression and anxiety
offer a rich lens into these conditions largely congruent with the
psychological literature, and that images on Twitter allow inferences about the
mental health status of users.Comment: ICWSM 201
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