1,213 research outputs found
Breadth analysis of Online Social Networks
This thesis is mainly motivated by the analysis, understanding, and prediction of human behaviour
by means of the study of their digital fingeprints. Unlike a classical PhD thesis, where
you choose a topic and go further on a deep analysis on a research topic, we carried out a breadth
analysis on the research topic of complex networks, such as those that humans create themselves
with their relationships and interactions. These kinds of digital communities where humans interact
and create relationships are commonly called Online Social Networks. Then, (i) we have
collected their interactions, as text messages they share among each other, in order to analyze the
sentiment and topic of such messages. We have basically applied the state-of-the-art techniques
for Natural Language Processing, widely developed and tested on English texts, in a collection
of Spanish Tweets and we compare the results. Next, (ii) we focused on Topic Detection, creating
our own classifier and applying it to the former Tweets dataset. The breakthroughs are two:
our classifier relies on text-graphs from the input text and we achieved a figure of 70% accuracy,
outperforming previous results. After that, (iii) we moved to analyze the network structure (or
topology) and their data values to detect outliers. We hypothesize that in social networks there
is a large mass of users that behaves similarly, while a reduced set of them behave in a different
way. However, specially among this last group, we try to separate those with high activity, or
low activity, or any other paramater/feature that make them belong to different kind of outliers.
We aim to detect influential users in one of these outliers set. We propose a new unsupervised
method, Massive Unsupervised Outlier Detection (MUOD), labeling the outliers detected os of
shape, magnitude, amplitude or combination of those. We applied this method to a subset of
roughly 400 million Google+ users, identifying and discriminating automatically sets of outlier
users. Finally, (iv) we find interesting to address the monitorization of real complex networks.
We created a framework to dynamically adapt the temporality of large-scale dynamic networks,
reducing compute overhead by at least 76%, data volume by 60% and overall cloud costs by at
least 54%, while always maintaining accuracy above 88%.PublicadoPrograma de Doctorado en IngenierĂa MatemĂĄtica por la Universidad Carlos III de MadridPresidente: Rosa MarĂa Benito Zafrilla.- Secretario: Ăngel Cuevas RumĂn.- Vocal: JosĂ© Ernesto JimĂ©nez Merin
Detecting sociosemantic communities by applying social network analysis in tweets
International audienceVirtual social networks have led to a new way of communication that is different from the oral one, where the restriction of time and space generates new linguistic practices. Twitter, a medium for political and social discussion, can be analyzed to understand new ways of communication and to explore sociosemiotics aspects that come with the use of the hashtags and their relationship with other elements. This paper presents a quantitative study of tweets, around a fixed hashtag, in relation with other contents that users bring to connection. By calculating the frequency of terms, a table of nodes and edges is created to visualize tweets like graphs. Our study applies social network analysis that, going beyond mere topology, reveals relevant sociosemantic communities providing insights for the comparison of social and political movements
Twitter financial community sentiment and its predictive relationship to stock market movement
Twitter, one of the several major social media platforms, has been identified as an influential factor for financial markets by multiple academic and professional publications in recent years. The motivation of this study hinges on the growing popularity of the use of Twitter and the increasing prevalence of its influence among the financial investment community. This paper presents empirical evidence of the existence of a financial community on Twitter in which usersâ interests align with financial market-related topics. We establish a methodology to identify relevant Twitter users who form the financial community, and we also present the empirical findings of network characteristics of the financial community. We observe that this financial community behaves similarly to a small-world network, and we further identify groups of critical nodes and analyse their influence within the financial community based on several network centrality measures. Using a novel sentiment analysis algorithm, we construct a weighted sentiment measure using tweet messages from these critical nodes, and we discover that it is significantly correlated with the returns of the major financial market indices. By forming a financial community within the Twitter universe, we argue that the influential Twitter users within the financial community provide a proxy for the relationship between social sentiment and financial market movement. Hence, we conclude that the weighted sentiment constructed from these critical nodes within the financial community provides a more robust predictor of financial markets than the general social sentiment
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