902,597 research outputs found
A Motif-based Approach for Identifying Controversy
Among the topics discussed in Social Media, some lead to controversy. A
number of recent studies have focused on the problem of identifying controversy
in social media mostly based on the analysis of textual content or rely on
global network structure. Such approaches have strong limitations due to the
difficulty of understanding natural language, and of investigating the global
network structure. In this work we show that it is possible to detect
controversy in social media by exploiting network motifs, i.e., local patterns
of user interaction. The proposed approach allows for a language-independent
and fine- grained and efficient-to-compute analysis of user discussions and
their evolution over time. The supervised model exploiting motif patterns can
achieve 85% accuracy, with an improvement of 7% compared to baseline
structural, propagation-based and temporal network features
A literature survey of methods for analysis of subjective language
Subjective language is used to express attitudes and opinions towards things, ideas and people. While content and topic centred natural language processing is now part of everyday life, analysis of subjective aspects of natural language have until recently been largely neglected by the research community. The explosive growth of personal blogs, consumer opinion sites and social network applications in the last years, have however created increased interest in subjective language analysis. This paper provides an overview of recent research conducted in the area
Semantic industrial categorisation based on search engine index
Analysis of specialist language is one of the most pressing
problems when trying to build intelligent content analysis
system. Identifying the scope of the language used and then understanding the relationships between the language entities is a key problem. A semantic relationship analysis of the search engine index was devised and evaluated. Using search engine index provides us with access to the widest database of knowledge in any particular field (if not now, then surely in the future). Social network analysis of keywords collection seems to generate a viable list of the specialist terms and relationships among them. This approach has been tested in the engineering and medical sectors
Multilayer Network of Language: a Unified Framework for Structural Analysis of Linguistic Subsystems
Recently, the focus of complex networks research has shifted from the
analysis of isolated properties of a system toward a more realistic modeling of
multiple phenomena - multilayer networks. Motivated by the prosperity of
multilayer approach in social, transport or trade systems, we propose the
introduction of multilayer networks for language. The multilayer network of
language is a unified framework for modeling linguistic subsystems and their
structural properties enabling the exploration of their mutual interactions.
Various aspects of natural language systems can be represented as complex
networks, whose vertices depict linguistic units, while links model their
relations. The multilayer network of language is defined by three aspects: the
network construction principle, the linguistic subsystem and the language of
interest. More precisely, we construct a word-level (syntax, co-occurrence and
its shuffled counterpart) and a subword level (syllables and graphemes) network
layers, from five variations of original text (in the modeled language). The
obtained results suggest that there are substantial differences between the
networks structures of different language subsystems, which are hidden during
the exploration of an isolated layer. The word-level layers share structural
properties regardless of the language (e.g. Croatian or English), while the
syllabic subword level expresses more language dependent structural properties.
The preserved weighted overlap quantifies the similarity of word-level layers
in weighted and directed networks. Moreover, the analysis of motifs reveals a
close topological structure of the syntactic and syllabic layers for both
languages. The findings corroborate that the multilayer network framework is a
powerful, consistent and systematic approach to model several linguistic
subsystems simultaneously and hence to provide a more unified view on language
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Face-to-face and online interactions - is a task a task?
This study contrasts two different ways of analysing interaction and participation in language learning tutorials: Social network analysis of frequency and QSR analysis of type of interaction. One task from three German beginners' language tutorials (one delivered face-to-face, the other two online) is analysed. A description of the background and method of the study is provided together with some examples of the findings. As this is work in progress, only tentative conclusions can be provided at this stage
Exploiting Social Network Structure for Person-to-Person Sentiment Analysis
Person-to-person evaluations are prevalent in all kinds of discourse and
important for establishing reputations, building social bonds, and shaping
public opinion. Such evaluations can be analyzed separately using signed social
networks and textual sentiment analysis, but this misses the rich interactions
between language and social context. To capture such interactions, we develop a
model that predicts individual A's opinion of individual B by synthesizing
information from the signed social network in which A and B are embedded with
sentiment analysis of the evaluative texts relating A to B. We prove that this
problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss
Markov random field, and we show that this implementation outperforms text-only
and network-only versions in two very different datasets involving
community-level decision-making: the Wikipedia Requests for Adminship corpus
and the Convote U.S. Congressional speech corpus
Making "fetch" happen: The influence of social and linguistic context on nonstandard word growth and decline
In an online community, new words come and go: today's "haha" may be replaced
by tomorrow's "lol." Changes in online writing are usually studied as a social
process, with innovations diffusing through a network of individuals in a
speech community. But unlike other types of innovation, language change is
shaped and constrained by the system in which it takes part. To investigate the
links between social and structural factors in language change, we undertake a
large-scale analysis of nonstandard word growth in the online community Reddit.
We find that dissemination across many linguistic contexts is a sign of growth:
words that appear in more linguistic contexts grow faster and survive longer.
We also find that social dissemination likely plays a less important role in
explaining word growth and decline than previously hypothesized
Mario, Luigi and Dave: the effect of language on the social structure of a bilingual online mobile game
In this paper, we explore the structure of a social community built in an online game that was released in two languages, specifically examining the behaviours of players involved in inter-lingual interaction. This asynchronous social game was released simultaneously in Italian and English. The player base was seeded with English and Italian players but allowed to grow organically without restriction. Despite the built-in segregation by language, we found that the entire player-base formed into a single social network and developed strategies for overcoming the challenges faced by a multi-lingual game community.
Using Network Analysis, we break down the community in the game based on language and play style. We demonstrate that the behaviour of both English and Italian players was equivalent, and that play style had no effect on the likelihood of players deliberately engaging in inter-lingual communication.
In the context of the strategies used by the players in our experiment, we discuss game design patterns that provide incentives for users to behave more socially and how to create tools to enable the players to cross the lingual and cultural barriers in online games
Using ontology engineering for understanding needs and allocating resources in web-based industrial virtual collaboration systems
In many interactions in cross-industrial and inter-industrial collaboration, analysis and understanding of relative specialist and non-specialist language is one of the most pressing challenges when trying to build multi-party, multi-disciplinary collaboration system. Hence, identifying the scope of the language used and then understanding the relationships between the language entities are key problems. In computer science, ontologies are used to provide a common vocabulary for a domain of interest together with descriptions of the meaning of terms and relationships between them, like in an encyclopedia. These, however, often lack the fuzziness required for human orientated systems. This paper uses an engineering sector business collaboration system (www.wmccm.co.uk) as a case study to illustrate the issues. The purpose of this paper is to introduce a novel ontology engineering methodology, which generates structurally enriched cross domain ontologies economically, quickly and reliably. A semantic relationship analysis of the Google Search Engine Index was devised and evaluated. Using Semantic analysis seems to generate a viable list of subject terms. A social network analysis of the semantically derived terms was conducted to generate a decision support network with rich relationships between terms. The derived ontology was quicker to generate, provided richer internal relationships and relied far less on expert contribution. More importantly, it improved the collaboration matching capability of WMCCM
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