Popularity Evolution of Professional Users on Facebook

Popularity in social media is an important objective for professional users (e.g. companies, celebrities, and public figures, etc). A simple yet prominent metric utilized to measure the popularity of a user is the number of fans or followers she succeed to attract to her page. Popularity is influenced by several factors which identifying them is an interesting research topic. This paper aims to understand this phenomenon in social media by exploring the popularity evolution for professional users in Facebook. To this end, we implemented a crawler and monitor the popularity evolution trend of 8k most popular professional users on Facebook over a period of 14 months. The collected dataset includes around 20 million popularity values and 43 million posts. We characterized different popularity evolution patterns by clustering the users temporal number of fans and study them from various perspectives including their categories and level of activities. Our observations show that being active and famous correlate positively with the popularity trend

TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data

Stock market forecasting has been a challenging part for many analysts and researchers. Trend analysis, statistical techniques, and movement indicators have traditionally been used to predict stock price movements, but text extraction has emerged as a promising method in recent years. The use of neural networks, especially recurrent neural networks, is abundant in the literature. In most studies, the impact of different users was considered equal or ignored, whereas users can have other effects. In the current study, we will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach, including the characteristics of each individual user, their impact on each other, and their impact on the market, to predict market direction more accurately. Dow Jones 30 index has been used in current work. The accuracy obtained for predicting daily stock changes of Apple is based on various models, closed to over 95\% and for the other stocks is significant. Our results indicate the effectiveness of TM-vector in predicting stock market direction.Comment: 24 pag

Analyse du modèle de popularité de l'utilisateur et de la prédiction d'engagement en les réseaux sociaux en ligne

Nowadays, social media has widely affected every aspect of human life. The most significant change in people's behavior after emerging Online Social Networks (OSNs) is their communication method and its range. Having more connections on OSNs brings more attention and visibility to people, where it is called popularity on social media. Depending on the type of social network, popularity is measured by the number of followers, friends, retweets, likes, and all those other metrics that is used to calculate engagement. Studying the popularity behavior of users and published contents on social media and predicting its future status are the important research directions which benefit different applications such as recommender systems, content delivery networks, advertising campaign, election results prediction and so on. This thesis addresses the analysis of popularity behavior of OSN users and their published posts in order to first, identify the popularity trends of users and posts and second, predict their future popularity and engagement level for published posts by users. To this end, i) the popularity evolution of ONS users is studied using a dataset of 8K Facebook professional users collected by an advanced crawler. The collected dataset includes around 38 million snapshots of users' popularity values and 64 million published posts over a period of 4 years. Clustering temporal sequences of users' popularity values led to identifying different and interesting popularity evolution patterns. The identified clusters are characterized by analyzing the users' business sector, called category, their activity level, and also the effect of external events. Then ii) the thesis focuses on the prediction of user engagement on the posts published by users on OSNs. A novel prediction model is proposed which takes advantage of Point-wise Mutual Information (PMI) and predicts users' future reaction to newly published posts. Finally, iii) the proposed model is extended to get benefits of representation learning and predict users' future engagement on each other's posts. The proposed prediction approach extracts user embedding from their reaction history instead of using conventional feature extraction methods. The performance of the proposed model proves that it outperforms conventional learning methods available in the literature. The models proposed in this thesis, not only improves the reaction prediction models to exploit representation learning features instead of hand-crafted features but also could help news agencies, advertising campaigns, content providers in CDNs, and recommender systems to take advantage of more accurate prediction results in order to improve their user servicesDe nos jours, les médias sociaux ont largement affecté tous les aspects de la vie humaine. Le changement le plus significatif dans le comportement des gens après l'émergence des réseaux sociaux en ligne (OSNs) est leur méthode de communication et sa portée. Avoir plus de connexions sur les OSNs apporte plus d'attention et de visibilité aux gens, où cela s'appelle la popularité sur les médias sociaux. Selon le type de réseau social, la popularité se mesure par le nombre d'adeptes, d'amis, de retweets, de goûts et toutes les autres mesures qui servaient à calculer l'engagement. L'étude du comportement de popularité des utilisateurs et des contenus publiés sur les médias sociaux et la prédiction de leur statut futur sont des axes de recherche importants qui bénéficient à différentes applications telles que les systèmes de recommandation, les réseaux de diffusion de contenu, les campagnes publicitaires, la prévision des résultats des élections, etc. Cette thèse porte sur l'analyse du comportement de popularité des utilisateurs d'OSN et de leurs messages publiés afin, d'une part, d'identifier les tendances de popularité des utilisateurs et des messages et, d'autre part, de prévoir leur popularité future et leur niveau d'engagement pour les messages publiés par les utilisateurs. A cette fin, i) l'évolution de la popularité des utilisateurs de l'ONS est étudiée à l'aide d'un ensemble de données d'utilisateurs professionnels 8K Facebook collectées par un crawler avancé. L'ensemble de données collectées comprend environ 38 millions d'instantanés des valeurs de popularité des utilisateurs et 64 millions de messages publiés sur une période de 4 ans. Le regroupement des séquences temporelles des valeurs de popularité des utilisateurs a permis d'identifier des modèles d'évolution de popularité différents et intéressants. Les grappes identifiées sont caractérisées par l'analyse du secteur d'activité des utilisateurs, appelé catégorie, leur niveau d'activité, ainsi que l'effet des événements externes. Ensuite ii) la thèse porte sur la prédiction de l'engagement des utilisateurs sur les messages publiés par les utilisateurs sur les OSNs. Un nouveau modèle de prédiction est proposé qui tire parti de l'information mutuelle par points (PMI) et prédit la réaction future des utilisateurs aux messages nouvellement publiés. Enfin, iii) le modèle proposé est élargi pour tirer profit de l'apprentissage de la représentation et prévoir l'engagement futur des utilisateurs sur leurs postes respectifs. L'approche de prédiction proposée extrait l'intégration de l'utilisateur de son historique de réaction au lieu d'utiliser les méthodes conventionnelles d'extraction de caractéristiques. La performance du modèle proposé prouve qu'il surpasse les méthodes d'apprentissage conventionnelles disponibles dans la littérature. Les modèles proposés dans cette thèse, non seulement déplacent les modèles de prédiction de réaction vers le haut pour exploiter les fonctions d'apprentissage de la représentation au lieu de celles qui sont faites à la main, mais pourraient également aider les nouvelles agences, les campagnes publicitaires, les fournisseurs de contenu dans les CDN et les systèmes de recommandation à tirer parti de résultats de prédiction plus précis afin d'améliorer leurs services aux utilisateur

An Iranian genomic sequence of Beet mosaic virus provides insights into diversity and evolution of the world population

Beet mosaic virus (BtMV), the only Potyvirus known to infect sugar beet, occurs worldwide in beet crops. The full genome sequencing of a BtMV isolate from Iran (Ir-VRU), enabled us to better understand the evolutionary history of this virus. Selection analysis suggested that BtMV evolution is mainly under negative selection but its strength varies in different proteins with the multifunctional proteins under strongest selection. Recombination has played a major role in the evolution of the BtMVs; only the Ir-VRU and USA isolates show no evidence of recombination. The ML phylogenies of BtMVs from coat protein and full sequences were completely congruent. The primary divergence of the BtMV phylogeny is into USA and Eurasian lineages, and the latter then divides to form a cluster only found in Iran, and a sister cluster that includes all the European and Chinese isolates. A simple patristic dating method estimated that the primary divergence of the BtMV population was only 360 (range 260–490) years ago, suggesting an emergence during the development of sugar beet as a crop over the past three centuries rather than with the use of leaf beet as a vegetable for at least 2000 years

User reactions prediction using embedding features

International audienceBy the massive available people data in social media, many digital service providers exploit widely this information to improve their services by predicting future requirements of their customers. This prediction mainly needs to study users' previous behavior and interactions and identify their preferences to provide rigorous recommendations that fulfill their requirements more favorably. Meanwhile, experiments show the prediction methods which exploit representation learning instead of traditional hand-crafted features accomplish better results and more precise predictions. In this study, we take advantage of representation learning method to predict user's future interactions by extracting users embeddings from their reactions history and exploit them in predicting future reactions. In this approach, users embeddings are used in a neural network designed with one-hidden layer and a softmax function in the end layer in order to predict users reactions. The proposed method is evaluated when user embeddings come from two different sources; users reactions history and random walks on the user network. The performance of the method has been evaluated by using a large Flickr dataset including more than 2M users and 11M users reactions sequences. The results show outperforming of the prediction method when it uses the history of user reactions to derive user embedding

Diatom-guided bone healing via a hybrid natural scaffold

Bone tissue engineering (BTE) involves the design of three-dimensional (3D) scaffolds that aim to address current challenges of bone defect healing, such as limited donor availability, disease transmission risks, and the necessity for multiple invasive surgeries. Scaffolds can mimic natural bone structure to accelerate the mechanisms involved in the healing process. Herein, a crosslinked combination of biopolymers, including gelatin (GEL), chitosan (CS), and hyaluronic acid (HA), loaded with diatom (Di) and β-sitosterol (BS), is used to produce GCH-Di-S scaffold by freeze-drying method. The GCH scaffold possesses a uniform structure, is biodegradable and biocompatible, and exhibits high porosity and interconnected pores, all required for effective bone repair. The incorporation of Di within the scaffold contributes to the adjustment of porosity and degradation, as well as effectively enhancing the mechanical property and biomineralization. In vivo studies have confirmed the safety of the scaffold and its potential to stimulate the creation of new bone tissue. This is achieved by providing an osteoconductive platform for cell attachment, prompting calcification, and augmenting the proliferation of osteoblasts, which further contributes to angiogenesis and anti-inflammatory effects of BS

Determination of triamterene in human plasma and urine after its cloud point extraction

A new analytical approach was developed involving cloud point extraction (CPE) and spectrofluorimetric determination of triamterene (TM) in biological fluids. A urine or plasma sample was prepared and adjusted to pH 7, then TM was quickly extracted using CPE, using 0.05% (w/v) of Triton X-114 as the extractant. The main factors that affected the extraction efficiency (the pH of the sample, the Triton X-114 concentration, the addition of salt, the extraction time and temperature, and the centrifugation time and speed) were studied and optimized. The method gave calibration curves for TM with good linearities and correlation coefficients (r) higher than 0.99. The method showed good precision and accuracy, with intra- and inter-assay precisions of less than 8.50% at all concentrations. Standard addition recovery tests were carried out, and the recoveries ranged from 94.7% to 114%. The limits of detection and quantification were 3.90 and 11.7 µg L-1, respectively, for urine and 5.80 and 18.0 µg L-1, respectively, for plasma. The newly developed, environmentally friendly method was successfully used to extract and determine TM in human urine samples

Diatom-guided bone healing via a hybrid natural scaffold

Bone tissue engineering (BTE) involves the design of three-dimensional (3D) scaffolds that aim to address current challenges of bone defect healing, such as limited donor availability, disease transmission risks, and the necessity for multiple invasive surgeries. Scaffolds can mimic natural bone structure to accelerate the mechanisms involved in the healing process. Herein, a crosslinked combination of biopolymers, including gelatin (GEL), chitosan (CS), and hyaluronic acid (HA), loaded with diatom (Di) and β-sitosterol (BS), is used to produce GCH-Di-S scaffold by freeze-drying method. The GCH scaffold possesses a uniform structure, is biodegradable and biocompatible, and exhibits high porosity and interconnected pores, all required for effective bone repair. The incorporation of Di within the scaffold contributes to the adjustment of porosity and degradation, as well as effectively enhancing the mechanical property and biomineralization. In vivo studies have confirmed the safety of the scaffold and its potential to stimulate the creation of new bone tissue. This is achieved by providing an osteoconductive platform for cell attachment, prompting calcification, and augmenting the proliferation of osteoblasts, which further contributes to angiogenesis and anti-inflammatory effects of BS