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Department of
Computational Perception
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WEB & SOCIAL MEDIA
Analysis and Prediction of User Traits and Behavior
This
group of topics focuses on analyzing
connections between social media data and characteristics of the users
as well as predicting
these characteristics by applying machine learning
techniques. Features extracted from social media data may
include metadata about
the user (e.g., number of tweets per day or number of friends) or
aspects extracted via natural
language processing. Also shared multimedia material
(e.g., images and video) or sensor
data (e.g., from smart phones) can be analyzed and
incorporated. User characteristics can be of very different nature, for
instance,
behavioral or consumption patterns, taste (music, movies, web
pages, etc.), age, gender, location, or even personality traits. For
analysis and prediction, methods from statistics and machine learning,
(e.g.,
correlation analysis and classification, respectively), should be
investigated and their performane thoroughly evaluated. References:
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Trend and Popularity Prediction
Usage
of social media has experienced an incredible increase during the last
couple of years. Nowadays, people create, share, consume, and comment
on all kinds of multimedia items through online social networks and
platforms. Predicting whether a particular item will become popular or
not is a hot topic both in academia and industry. Possible topics for
practical projects and Master's theses in this context include
elaborating new techniques to harvest
a variety of data sources (e.g., multimedia content
descriptors, microblogs, social network structure, or consumption
histories), researching models
that describe the popularity
of items (songs, videos, persons,
scientific
publications, etc.), designing computational
features that will serve as
predictors
for future popularity in machine
learning algorithms, and evaluating
them in comprehensive experiments. Another related topic is the
analysis and prediction of "popularity
flows", i.e., where trends emerge and how they spread
temporally and spatially. References:
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Opinion Mining and Sentiment Analysis
"Sentiment
analysis or opinion mining refers to the application of natural
language processing, computational linguistics, and text analytics to
identify and extract
subjective information in source materials.
Generally speaking, sentiment analysis aims to determine the attitude
of a speaker or a writer with respect to some topic or the
overall
contextual polarity of a document. The attitude may be his or her
judgement or evaluation (see appraisal theory), affective state (that
is to say, the emotional state of the author when writing), or the
intended emotional communication (that is to say, the emotional effect
the author wishes to have on the reader)." [Wikipedia] Examples for projects:
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Recommendation and Discovery
Exploiting
the web and social media, one can identify many potential sources for
discovery and
recommendation of similar
items (music pieces, movies, news, etc.). While topics
related to all kinds of multimedia
material can be worked on, in the context of music, similar
artists or songs are of highest interest. Sources to be
considered include word
patterns on music pages, user-generated
tags, ratings,
tweets,
search
engine page counts, peer-to-peer
networks, or playlists.
Mining these sources can not only give information on the artistic
context of music and help estimating similarity, but also to defer
measures and indicators such as popularity,
hotness,
novelty,
diversity,
and genre
prototypicality. In this context, topics related to user modeling and personalized as well
as user-aware retrieval
and recommendation can be considered, too. Examples for projects:
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last edited by ms on Sep 24, 2015