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*Soc2Net: International workshop on Modeling and mining Social-Media-driven
Complex Networks  @ ICWSM-19 *
June 11, 2019, Munich, Germany

https://urldefense.proofpoint.com/v2/url?u=http-3A__events.dimes.unical.it_soc2net&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=D425-gA0fm9dw7TQxlvm9UNjKr67Sy2yMD94EyCSebw&s=HIoCIK41CLCMz8-FRjlWn1clbe91KxkDwnwYEPRYdfE&e=


*PROGRAM ONLINE AT:* https://urldefense.proofpoint.com/v2/url?u=http-3A__events.dimes.unical.it_soc2net_program_&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=D425-gA0fm9dw7TQxlvm9UNjKr67Sy2yMD94EyCSebw&s=aUEIVExnVzDC4Z67NWpAY45QTYgMzJkSZr-KrxRTAPI&e=

*CONTEXT*

The growing availability of multi-faceted social media data gives rise to
unprecedented opportunities for unveiling complex real-world online
behaviors. This also supports  the proliferation of complex network models
where the expressive power of the graph-based relational structure  is
enhanced through exposing several types of  features that are   peculiar of
the social media platforms. This workshop aims to explore  innovative
methods that are designed to improve our understanding of behaviors and
relations underlying feature-rich networks built upon social media, here
called social-media-driven complex networks. Exemplary network models of
such kind include heterogeneous,   multilayer/multiplex/multirelational
networks, temporal, location-aware, and probabilistic networks, and any
other type of data-driven network that can be inferred from social media
data contexts.
The aim of the Soc2Net workshop, that will be held in conjunction with The
13th International AAAI Conference on Web and Social Media (ICWSM-2019) in
Munich (Germany), is to bring together researchers and practitioners from
around the world interested in 1) exploring different perspectives and
approaches to mine social-media-driven complex networks, 2) analyzing user
behavior and evolution in social-media-driven complex networks, and 3)
building models and frameworks for evaluating the respective approaches.
Authors are encouraged to evaluate their models, methods, metrics and
algorithms on real-world social networks built upon publicly available
datasets, e.g., relying on the datasets from the previous editions of ICWSM
which are released as openly available community resources. We solicit
interdisciplinary submissions focusing on topics of interest to different
research communities, including social science, economics and digital
humanities.


*INVITED TALK*

https://urldefense.proofpoint.com/v2/url?u=http-3A__events.dimes.unical.it_soc2net_invited-2Dtalk_&d=DwIBaQ&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=D425-gA0fm9dw7TQxlvm9UNjKr67Sy2yMD94EyCSebw&s=btscHLRRyhp2te9wP7PSZyzxX-m4U0QeveqIh7Pw5rQ&e=

*Robert West:* Message Distortion in Information Cascades

Information diffusion is usually modeled as a process in which immutable
pieces of information propagate over a network. In reality, however,
messages are not immutable, but may be morphed with every step, potentially
entailing large cumulative distortions. This process may lead to
misinformation even in the absence of malevolent actors, and understanding
it is crucial for modeling and improving online information systems. Here,
we perform a controlled, crowdsourced experiment in which we simulate the
propagation of information from medical research papers. Starting from the
original abstracts, crowd workers iteratively shorten previously produced
summaries to increasingly smaller lengths. We also collect control
summaries where the original abstract is compressed directly to the final
target length. Comparing cascades to controls allows us to separate the
effect of the length constraint from that of accumulated distortion. Via
careful manual coding, we annotate lexical and semantic units in the
medical abstracts and track them along cascades. We find that iterative
summarization has a negative impact due to the accumulation of error, but
that high-quality intermediate summaries result in less distorted messages
than in the control case. Different types of information behave
differently; in particular, the conclusion of a medical abstract (i.e., its
key message) is distorted most. Finally, we compare extractive with
abstractive summaries, finding that the latter are less prone to semantic
distortion. Overall, this work is a first step in studying information
cascades without the assumption that disseminated content is immutable,
with implications on our understanding of the role of word-of-mouth effects
on the misreporting of science. (Joint work with Manoel Horta Ribeiro and
Kristina Gligoric)

*Robert West *is an assistant professor of Computer Science at EPFL, where
he heads the Data Science Lab. His research aims to understand, predict,
and enhance human behavior in social and information networks by developing
techniques in data science, data mining, network analysis, machine
learning, and natural language processing. He holds a PhD in computer
science from Stanford University.

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