63,678 research outputs found
Generating Navigable Semantic Maps from Social Sciences Corpora
It is now commonplace to observe that we are facing a deluge of online
information. Researchers have of course long acknowledged the potential value
of this information since digital traces make it possible to directly observe,
describe and analyze social facts, and above all the co-evolution of ideas and
communities over time. However, most online information is expressed through
text, which means it is not directly usable by machines, since computers
require structured, organized and typed information in order to be able to
manipulate it. Our goal is thus twofold: 1. Provide new natural language
processing techniques aiming at automatically extracting relevant information
from texts, especially in the context of social sciences, and connect these
pieces of information so as to obtain relevant socio-semantic networks; 2.
Provide new ways of exploring these socio-semantic networks, thanks to tools
allowing one to dynamically navigate these networks, de-construct and
re-construct them interactively, from different points of view following the
needs expressed by domain experts.Comment: in Digital Humanities 2015, Jun 2015, Sydney, Australia. Actes de la
Conf{\'e}rence Digital Humanities 2015. arXiv admin note: text overlap with
arXiv:1406.421
All the ties that bind. A socio-semantic network analysis of Twitter political discussions
Social media play a crucial role in what contemporary sociological reflections define as a ‘hybrid
media system’. Online spaces created by social media platforms resemble global public
squares hosting large-scale social networks populated by citizens, political leaders, parties
and organizations, journalists, activists and institutions that establish direct interactions and
exchange contents in a disintermediated fashion. In the last decade, an increasing number
of studies from researchers coming from different disciplines has approached the study of the
manifold facets of citizen participation in online political spaces. In most cases, these studies
have focused on the investigation of direct relationships amongst political actors. Conversely,
relatively less attention has been paid to the study of contents that circulate during online
discussions and how their diffusion contributes to building political identities. Even more
rarely, the study of social media contents has been investigated in connection with those concerning
social interactions amongst online users. To fill in this gap, my thesis work proposes
a methodological procedure consisting in a network-based, data-driven approach to both
infer communities of users with a similar communication behavior and to extract the most
prominent contents discussed within those communities. More specifically, my work focuses
on Twitter, a social media platform that is widely used during political debates. Groups
of users with a similar retweeting behavior - hereby referred to as discursive communities -
are identified starting with the bipartite network of Twitter verified users retweeted by nonverified
users. Once the discursive communities are obtained, the corresponding semantic
networks are identified by considering the co-occurrences of the hashtags that are present in
the tweets sent by their members.
The identification of discursive communities and the study of the related semantic networks
represent the starting point for exploring more in detail two specific conversations that took
place in the Italian Twittersphere: the former occured during the electoral campaign before
the 2018 Italian general elections and in the two weeks after Election day; the latter
centered on the issue of migration during the period May-November 2019. Regarding the
social analysis, the main result of my work is the identification of a behavior-driven picture
of discursive communities induced by the retweeting activity of Twitter users, rather than
determined by prior information on their political affiliation. Although these communities
do not necessarily match the political orientation of their users, they are closely related to
the evolution of the Italian political arena. As for the semantic analysis, this work sheds light
on the symbolic dimension of partisan dynamics. Different discursive communities are, in
fact, characterized by a peculiar conversational dynamics at both the daily and the monthly
time-scale. From a purely methodological aspect, semantic networks have been analyzed by
employing three (increasingly restrictive) benchmarks. The k-shell decomposition of both
filtered and non-filtered semantic networks reveals the presence of a core-periphery structure
providing information on the most debated topics within each discursive community and
characterizing the communication strategy of the corresponding political coalition
The Evolution of Wikipedia's Norm Network
Social norms have traditionally been difficult to quantify. In any particular
society, their sheer number and complex interdependencies often limit a
system-level analysis. One exception is that of the network of norms that
sustain the online Wikipedia community. We study the fifteen-year evolution of
this network using the interconnected set of pages that establish, describe,
and interpret the community's norms. Despite Wikipedia's reputation for
\textit{ad hoc} governance, we find that its normative evolution is highly
conservative. The earliest users create norms that both dominate the network
and persist over time. These core norms govern both content and interpersonal
interactions using abstract principles such as neutrality, verifiability, and
assume good faith. As the network grows, norm neighborhoods decouple
topologically from each other, while increasing in semantic coherence. Taken
together, these results suggest that the evolution of Wikipedia's norm network
is akin to bureaucratic systems that predate the information age.Comment: 22 pages, 9 figures. Matches published version. Data available at
http://bit.ly/wiki_nor
The topology of a discussion: the #occupy case
We analyse a large sample of the Twitter activity developed around the social
movement 'Occupy Wall Street' to study the complex interactions between the
human communication activity and the semantic content of a discussion. We use a
network approach based on the analysis of the bipartite graph @Users-#Hashtags
and of its projections: the 'semantic network', whose nodes are hashtags, and
the 'users interest network', whose nodes are users In the first instance, we
find out that discussion topics (#hashtags) present a high heterogeneity, with
the distinct role of the communication hubs where most the 'opinion traffic'
passes through. In the second case, the self-organization process of users
activity leads to the emergence of two classes of communicators: the
'professionals' and the 'amateurs'. Moreover the network presents a strong
community structure, based on the differentiation of the semantic topics, and a
high level of structural robustness when a certain set of topics are censored
and/or accounts are removed. Analysing the characteristics the @Users-#Hashtags
network we can distinguish three phases of the discussion about the movement.
Each phase corresponds to specific moment of the movement: from declaration of
intent, organisation and development and the final phase of political
reactions. Each phase is characterised by the presence of specific #hashtags in
the discussion. Keywords: Twitter, Network analysisComment: 13 pages, 9 figure
Semantic Variation in Online Communities of Practice
We introduce a framework for quantifying semantic variation of common words
in Communities of Practice and in sets of topic-related communities. We show
that while some meaning shifts are shared across related communities, others
are community-specific, and therefore independent from the discussed topic. We
propose such findings as evidence in favour of sociolinguistic theories of
socially-driven semantic variation. Results are evaluated using an independent
language modelling task. Furthermore, we investigate extralinguistic features
and show that factors such as prominence and dissemination of words are related
to semantic variation.Comment: 13 pages, Proceedings of the 12th International Conference on
Computational Semantics (IWCS 2017
Challenges in Bridging Social Semantics and Formal Semantics on the Web
This paper describes several results of Wimmics, a research lab which names
stands for: web-instrumented man-machine interactions, communities, and
semantics. The approaches introduced here rely on graph-oriented knowledge
representation, reasoning and operationalization to model and support actors,
actions and interactions in web-based epistemic communities. The re-search
results are applied to support and foster interactions in online communities
and manage their resources
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
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