32,198 research outputs found
Collective emotions online and their influence on community life
E-communities, social groups interacting online, have recently become an
object of interdisciplinary research. As with face-to-face meetings, Internet
exchanges may not only include factual information but also emotional
information - how participants feel about the subject discussed or other group
members. Emotions are known to be important in affecting interaction partners
in offline communication in many ways. Could emotions in Internet exchanges
affect others and systematically influence quantitative and qualitative aspects
of the trajectory of e-communities? The development of automatic sentiment
analysis has made large scale emotion detection and analysis possible using
text messages collected from the web. It is not clear if emotions in
e-communities primarily derive from individual group members' personalities or
if they result from intra-group interactions, and whether they influence group
activities. We show the collective character of affective phenomena on a large
scale as observed in 4 million posts downloaded from Blogs, Digg and BBC
forums. To test whether the emotions of a community member may influence the
emotions of others, posts were grouped into clusters of messages with similar
emotional valences. The frequency of long clusters was much higher than it
would be if emotions occurred at random. Distributions for cluster lengths can
be explained by preferential processes because conditional probabilities for
consecutive messages grow as a power law with cluster length. For BBC forum
threads, average discussion lengths were higher for larger values of absolute
average emotional valence in the first ten comments and the average amount of
emotion in messages fell during discussions. Our results prove that collective
emotional states can be created and modulated via Internet communication and
that emotional expressiveness is the fuel that sustains some e-communities.Comment: 23 pages including Supporting Information, accepted to PLoS ON
Demography-based adaptive network model reproduces the spatial organization of human linguistic groups
The distribution of human linguistic groups presents a number of interesting
and non-trivial patterns. The distributions of the number of speakers per
language and the area each group covers follow log-normal distributions, while
population and area fulfill an allometric relationship. The topology of
networks of spatial contacts between different linguistic groups has been
recently characterized, showing atypical properties of the degree distribution
and clustering, among others. Human demography, spatial conflicts, and the
construction of networks of contacts between linguistic groups are mutually
dependent processes. Here we introduce an adaptive network model that takes all
of them into account and successfully reproduces, using only four model
parameters, not only those features of linguistic groups already described in
the literature, but also correlations between demographic and topological
properties uncovered in this work. Besides their relevance when modeling and
understanding processes related to human biogeography, our adaptive network
model admits a number of generalizations that broaden its scope and make it
suitable to represent interactions between agents based on population dynamics
and competition for space
Artificial Sequences and Complexity Measures
In this paper we exploit concepts of information theory to address the
fundamental problem of identifying and defining the most suitable tools to
extract, in a automatic and agnostic way, information from a generic string of
characters. We introduce in particular a class of methods which use in a
crucial way data compression techniques in order to define a measure of
remoteness and distance between pairs of sequences of characters (e.g. texts)
based on their relative information content. We also discuss in detail how
specific features of data compression techniques could be used to introduce the
notion of dictionary of a given sequence and of Artificial Text and we show how
these new tools can be used for information extraction purposes. We point out
the versatility and generality of our method that applies to any kind of
corpora of character strings independently of the type of coding behind them.
We consider as a case study linguistic motivated problems and we present
results for automatic language recognition, authorship attribution and self
consistent-classification.Comment: Revised version, with major changes, of previous "Data Compression
approach to Information Extraction and Classification" by A. Baronchelli and
V. Loreto. 15 pages; 5 figure
Processing of false belief passages during natural story comprehension: An fMRI study
The neural correlates of theory of mind (ToM) are typically studied using paradigms which require participants to draw explicit, task-related inferences (e.g., in the false belief task). In a natural setup, such as listening to stories, false belief mentalizing occurs incidentally as part of narrative processing. In our experiment, participants listened to auditorily presented stories with false belief passages (implicit false belief processing) and immediately after each story answered comprehension questions (explicit false belief processing), while neural responses were measured with functional magnetic resonance imaging (fMRI). All stories included (among other situations) one false belief condition and one closely matched control condition. For the implicit ToM processing, we modeled the hemodynamic response during the false belief passages in the story and compared it to the hemodynamic response during the closely matched control passages. For implicit mentalizing, we found activation in typical ToM processing regions, that is the angular gyrus (AG), superior medial frontal gyrus (SmFG), precuneus (PCUN), middle temporal gyrus (MTG) as well as in the inferior frontal gyrus (IFG) billaterally. For explicit ToM, we only found AG activation. The conjunction analysis highlighted the left AG and MTG as well as the bilateral IFG as overlapping ToM processing regions for both implicit and explicit modes. Implicit ToM processing during listening to false belief passages, recruits the left SmFG and billateral PCUN in addition to the “mentalizing network” known form explicit processing tasks
Image and interpretation using artificial intelligence to read ancient Roman texts
The ink and stylus tablets discovered at the Roman Fort of Vindolanda are a unique resource for scholars of ancient history. However, the stylus tablets have proved particularly difficult to read. This paper describes a system that assists expert papyrologists in the interpretation of the Vindolanda writing tablets. A model-based approach is taken that relies on models of the written form of characters, and statistical modelling of language, to produce plausible interpretations of the documents. Fusion of the contributions from the language, character, and image feature models is achieved by utilizing the GRAVA agent architecture that uses Minimum Description Length as the basis for information fusion across semantic levels. A system is developed that reads in image data and outputs plausible interpretations of the Vindolanda tablets
LAF-Fabric: a data analysis tool for Linguistic Annotation Framework with an application to the Hebrew Bible
The Linguistic Annotation Framework (LAF) provides a general, extensible
stand-off markup system for corpora. This paper discusses LAF-Fabric, a new
tool to analyse LAF resources in general with an extension to process the
Hebrew Bible in particular. We first walk through the history of the Hebrew
Bible as text database in decennium-wide steps. Then we describe how LAF-Fabric
may serve as an analysis tool for this corpus. Finally, we describe three
analytic projects/workflows that benefit from the new LAF representation:
1) the study of linguistic variation: extract cooccurrence data of common
nouns between the books of the Bible (Martijn Naaijer); 2) the study of the
grammar of Hebrew poetry in the Psalms: extract clause typology (Gino Kalkman);
3) construction of a parser of classical Hebrew by Data Oriented Parsing:
generate tree structures from the database (Andreas van Cranenburgh)
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