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

Bisociative knowledge discovery for microarray data analysis

Peer reviewe

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