31,432 research outputs found
Active learning in annotating micro-blogs dealing with e-reputation
Elections unleash strong political views on Twitter, but what do people
really think about politics? Opinion and trend mining on micro blogs dealing
with politics has recently attracted researchers in several fields including
Information Retrieval and Machine Learning (ML). Since the performance of ML
and Natural Language Processing (NLP) approaches are limited by the amount and
quality of data available, one promising alternative for some tasks is the
automatic propagation of expert annotations. This paper intends to develop a
so-called active learning process for automatically annotating French language
tweets that deal with the image (i.e., representation, web reputation) of
politicians. Our main focus is on the methodology followed to build an original
annotated dataset expressing opinion from two French politicians over time. We
therefore review state of the art NLP-based ML algorithms to automatically
annotate tweets using a manual initiation step as bootstrap. This paper focuses
on key issues about active learning while building a large annotated data set
from noise. This will be introduced by human annotators, abundance of data and
the label distribution across data and entities. In turn, we show that Twitter
characteristics such as the author's name or hashtags can be considered as the
bearing point to not only improve automatic systems for Opinion Mining (OM) and
Topic Classification but also to reduce noise in human annotations. However, a
later thorough analysis shows that reducing noise might induce the loss of
crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science -
Vol 3 - Contextualisation digitale - 201
Political Homophily in Independence Movements: Analysing and Classifying Social Media Users by National Identity
Social media and data mining are increasingly being used to analyse political
and societal issues. Here we undertake the classification of social media users
as supporting or opposing ongoing independence movements in their territories.
Independence movements occur in territories whose citizens have conflicting
national identities; users with opposing national identities will then support
or oppose the sense of being part of an independent nation that differs from
the officially recognised country. We describe a methodology that relies on
users' self-reported location to build large-scale datasets for three
territories -- Catalonia, the Basque Country and Scotland. An analysis of these
datasets shows that homophily plays an important role in determining who people
connect with, as users predominantly choose to follow and interact with others
from the same national identity. We show that a classifier relying on users'
follow networks can achieve accurate, language-independent classification
performances ranging from 85% to 97% for the three territories.Comment: Accepted for publication in IEEE Intelligent System
Demographic Inference and Representative Population Estimates from Multilingual Social Media Data
Social media provide access to behavioural data at an unprecedented scale and
granularity. However, using these data to understand phenomena in a broader
population is difficult due to their non-representativeness and the bias of
statistical inference tools towards dominant languages and groups. While
demographic attribute inference could be used to mitigate such bias, current
techniques are almost entirely monolingual and fail to work in a global
environment. We address these challenges by combining multilingual demographic
inference with post-stratification to create a more representative population
sample. To learn demographic attributes, we create a new multimodal deep neural
architecture for joint classification of age, gender, and organization-status
of social media users that operates in 32 languages. This method substantially
outperforms current state of the art while also reducing algorithmic bias. To
correct for sampling biases, we propose fully interpretable multilevel
regression methods that estimate inclusion probabilities from inferred joint
population counts and ground-truth population counts. In a large experiment
over multilingual heterogeneous European regions, we show that our demographic
inference and bias correction together allow for more accurate estimates of
populations and make a significant step towards representative social sensing
in downstream applications with multilingual social media.Comment: 12 pages, 10 figures, Proceedings of the 2019 World Wide Web
Conference (WWW '19
Detecting Real-World Influence Through Twitter
In this paper, we investigate the issue of detecting the real-life influence
of people based on their Twitter account. We propose an overview of common
Twitter features used to characterize such accounts and their activity, and
show that these are inefficient in this context. In particular, retweets and
followers numbers, and Klout score are not relevant to our analysis. We thus
propose several Machine Learning approaches based on Natural Language
Processing and Social Network Analysis to label Twitter users as Influencers or
not. We also rank them according to a predicted influence level. Our proposals
are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art
ranking methods.Comment: 2nd European Network Intelligence Conference (ENIC), Sep 2015,
Karlskrona, Swede
Computational Sociolinguistics: A Survey
Language is a social phenomenon and variation is inherent to its social
nature. Recently, there has been a surge of interest within the computational
linguistics (CL) community in the social dimension of language. In this article
we present a survey of the emerging field of "Computational Sociolinguistics"
that reflects this increased interest. We aim to provide a comprehensive
overview of CL research on sociolinguistic themes, featuring topics such as the
relation between language and social identity, language use in social
interaction and multilingual communication. Moreover, we demonstrate the
potential for synergy between the research communities involved, by showing how
the large-scale data-driven methods that are widely used in CL can complement
existing sociolinguistic studies, and how sociolinguistics can inform and
challenge the methods and assumptions employed in CL studies. We hope to convey
the possible benefits of a closer collaboration between the two communities and
conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication:
18th February, 201
Noise or music? Investigating the usefulness of normalisation for robust sentiment analysis on social media data
In the past decade, sentiment analysis research has thrived, especially on social media. While this data genre is suitable to extract opinions and sentiment, it is known to be noisy. Complex normalisation methods have been developed to transform noisy text into its standard form, but their effect on tasks like sentiment analysis remains underinvestigated. Sentiment analysis approaches mostly include spell checking or rule-based normalisation as preprocess- ing and rarely investigate its impact on the task performance. We present an optimised sentiment classifier and investigate to what extent its performance can be enhanced by integrating SMT-based normalisation as preprocessing. Experiments on a test set comprising a variety of user-generated content genres revealed that normalisation improves sentiment classification performance on tweets and blog posts, showing the model’s ability to generalise to other data genres
A Socio-Informatic Approach to Automated Account Classification on Social Media
Automated accounts on social media have become increasingly problematic. We
propose a key feature in combination with existing methods to improve machine
learning algorithms for bot detection. We successfully improve classification
performance through including the proposed feature.Comment: International Conference on Social Media and Societ
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