91 research outputs found
News Cohesiveness: an Indicator of Systemic Risk in Financial Markets
Motivated by recent financial crises significant research efforts have been
put into studying contagion effects and herding behaviour in financial markets.
Much less has been said about influence of financial news on financial markets.
We propose a novel measure of collective behaviour in financial news on the
Web, News Cohesiveness Index (NCI), and show that it can be used as a systemic
risk indicator. We evaluate the NCI on financial documents from large Web news
sources on a daily basis from October 2011 to July 2013 and analyse the
interplay between financial markets and financially related news. We
hypothesized that strong cohesion in financial news reflects movements in the
financial markets. Cohesiveness is more general and robust measure of systemic
risk expressed in news, than measures based on simple occurrences of specific
terms. Our results indicate that cohesiveness in the financial news is highly
correlated with and driven by volatility on the financial markets
Dynamics of online hate and misinformation
Online debates are often characterised by extreme polarisation and heated discussions among
users. The presence of hate speech online is becoming increasingly problematic, making necessary
the development of appropriate countermeasures. In this work, we perform hate speech detection
on a corpus of more than one million comments on YouTube videos through a machine learning
model, trained and fine-tuned on a large set of hand-annotated data. Our analysis shows that there
is no evidence of the presence of “pure haters”, meant as active users posting exclusively hateful
comments. Moreover, coherently with the echo chamber hypothesis, we find that users skewed
towards one of the two categories of video channels (questionable, reliable) are more prone to use
inappropriate, violent, or hateful language within their opponents’ community. Interestingly, users
loyal to reliable sources use on average a more toxic language than their counterpart. Finally, we find
that the overall toxicity of the discussion increases with its length, measured both in terms of the
number of comments and time. Our results show that, coherently with Godwin’s law, online debates
tend to degenerate towards increasingly toxic exchanges of views
Handling Disagreement in Hate Speech Modelling
Hate speech annotation for training machine learning models is an inherently ambiguous and subjective task. In this paper, we adopt a perspectivist approach to data annotation, model training and evaluation for hate speech classification. We first focus on the annotation process and argue that it drastically influences the final data quality. We then present three large hate speech datasets that incorporate annotator disagreement and use them to train and evaluate machine learning models. As the main point, we propose to evaluate machine learning models through the lens of disagreement by applying proper performance measures to evaluate both annotators’ agreement and models’ quality. We further argue that annotator agreement poses intrinsic limits to the performance achievable by models. When comparing models and annotators, we observed that they achieve consistent levels of agreement across datasets. We reflect upon our results and propose some methodological and ethical considerations that can stimulate the ongoing discussion on hate speech modelling and classification with disagreement
Efficacy of Limbal Stem Cell Transplantation in the Treatment of Recurrent Pterygium
Cilj rada je prikazati učinkovitost transplantacije limbalnih stanica (LSCT) kao načina liječenja recidivirajućih pterigija. Ukupno je 18 očiju s recidivirajućim pterigijem operirano tehnikom LSCT; 12 bolesnika je primarno bilo operirano tehnikom "bare sclera", 3 rotacijom konjunktive i 3 transplantacijom amnijske membrane. Nije bilo značajnijih intraoperacijskih komplikacija osim reverzibilnog edema konjunktivnog grafta kod 2 bolesnika, Tenonovog granuloma kod jednog bolesnika te hematoma također kod jednog bolesnika. Kod 16 bolesnika nije bilo recidiva pterigija tijekom vremena praćenja, dok se kod 2 bolesnika recidiv javio unutar 18 mjeseci. Zaključak rada je da je LSCT uspješna i sigurna metoda u liječenju recidivirajućih pterigija, iako je tehnički i vremenski dosta zahtjevna.The aim of the study was to assess the efficacy of limbal stem cell transplantation (LSCT) as a treatment for recurrent pterygium. Eighteen eyes with recurrent pterygium underwent LSCT. Twelve eyes had been previously operated by the "bare-sclera" technique, 3 by conjunctival rotation and 3 by amnion membrane transplantation. No serious intraoperative complications occurred, except for reversible conjunctival graft edema in 2 eyes, Tenon\u27s granuloma in one case, and hematoma under the graft in one case. In 16 eyes no pterygium recurrence was recorded during the follow up period. Two recurrences were recorded during 18 months after surgery. It is concluded that LSCT is a successful and safe yet time-consuming and technically demanding method in the management of recurrent pterygium
Multimodal Emotion Classification
Most NLP and Computer Vision tasks are limited to scarcity of labelled data.
In social media emotion classification and other related tasks, hashtags have
been used as indicators to label data. With the rapid increase in emoji usage
of social media, emojis are used as an additional feature for major social NLP
tasks. However, this is less explored in case of multimedia posts on social
media where posts are composed of both image and text. At the same time, w.e
have seen a surge in the interest to incorporate domain knowledge to improve
machine understanding of text. In this paper, we investigate whether domain
knowledge for emoji can improve the accuracy of emotion classification task. We
exploit the importance of different modalities from social media post for
emotion classification task using state-of-the-art deep learning architectures.
Our experiments demonstrate that the three modalities (text, emoji and images)
encode different information to express emotion and therefore can complement
each other. Our results also demonstrate that emoji sense depends on the
textual context, and emoji combined with text encodes better information than
considered separately. The highest accuracy of 71.98\% is achieved with a
training data of 550k posts.Comment: Accepted at the 2nd Emoji Workshop co-located with The Web Conference
201
A Semantics-Based Measure of Emoji Similarity
Emoji have grown to become one of the most important forms of communication
on the web. With its widespread use, measuring the similarity of emoji has
become an important problem for contemporary text processing since it lies at
the heart of sentiment analysis, search, and interface design tasks. This paper
presents a comprehensive analysis of the semantic similarity of emoji through
embedding models that are learned over machine-readable emoji meanings in the
EmojiNet knowledge base. Using emoji descriptions, emoji sense labels and emoji
sense definitions, and with different training corpora obtained from Twitter
and Google News, we develop and test multiple embedding models to measure emoji
similarity. To evaluate our work, we create a new dataset called EmoSim508,
which assigns human-annotated semantic similarity scores to a set of 508
carefully selected emoji pairs. After validation with EmoSim508, we present a
real-world use-case of our emoji embedding models using a sentiment analysis
task and show that our models outperform the previous best-performing emoji
embedding model on this task. The EmoSim508 dataset and our emoji embedding
models are publicly released with this paper and can be downloaded from
http://emojinet.knoesis.org/.Comment: This paper is accepted at Web Intelligence 2017 as a full paper, In
2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Leipzig,
Germany: ACM, 201
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