Will they come and will they stay? Online Social Networks and news consumption on external websites

This study explores the role of online engagement, homophily and social influence in explaining traffic and news consumption by social network users at an external news website. The authors jointly model visits and page views for a panel of users who registered with the news site using their Facebook accounts. In their model, the authors account for homophily using a latent space approach, and account for endogeneity, heterogeneity, and unobservable correlates. The results show that measures of an individual’s activity on Facebook are positively associated with that individual’s actions at the news site. In addition, knowing what a user’s Facebook friends do at the content website provides insights into a focal user’s behavior at that website, as visitors with friends who visit external news sites are more likely to visit the news website studied. In addition, news consumption (not just visits) also depends on friend’s actions but such an impact varies with the individual’s underlying browsing mode. We highlight the importance of social influence in news consumption and further show that homophily bias in news consumption is similar to prior research in other categories. Our study also highlights that visitors’ past browsing patterns are important predictors of future content consumption, although social network information significantly improves prediction beyond the effect of such more traditional behavioral metrics. Finally, we find that Managers can use readily available data for both prediction and targeting

The Evaluation of DyHATR Performance for Dynamic Heterogeneous Graphs

Dynamic heterogeneous graphs can represent real-world networks. Predicting links in these graphs is more complicated than in static graphs. Until now, research interest of link prediction has focused on static heterogeneous graphs or dynamically homogeneous graphs. A link prediction technique combining temporal RNN and hierarchical attention has recently emerged, called DyHATR. This method is claimed to be able to work on dynamic heterogeneous graphs by testing them on four publicly available data sets (Twitter, Math-Overflow, Ecomm, and Alibaba). However, after further analysis, it turned out that the four data sets did not meet the criteria of dynamic heterogeneous graphs. In the present work, we evaluated the performance of DyHATR on dynamic heterogeneous graphs. We conducted experiments with DyHATR based on the Yelp data set represented as a dynamic heterogeneous graph consisting of homogeneous subgraphs. The results show that DyHATR can be applied to identify link prediction on dynamic heterogeneous graphs by simultaneously capturing heterogeneous information and evolutionary patterns, and then considering them to carry out link predicition. Compared to the baseline method, the accuracy achieved by DyHATR is competitive, although the results can still be improved

The Evaluation of DyHATR Performance for Dynamic Heterogeneous Graphs

Dynamic heterogeneous graphs can represent real-world networks. Predicting links in these graphs is more complicated than in static graphs. Until now, research interest of link prediction has focused on static heterogeneous graphs or dynamically homogeneous graphs. A link prediction technique combining temporal RNN and hierarchical attention has recently emerged, called DyHATR. This method is claimed to be able to work on dynamic heterogeneous graphs by testing them on four publicly available data sets (Twitter, Math-Overflow, Ecomm, and Alibaba). However, after further analysis, it turned out that the four data sets did not meet the criteria of dynamic heterogeneous graphs. In the present work, we evaluated the performance of DyHATR on dynamic heterogeneous graphs. We conducted experiments with DyHATR based on the Yelp data set represented as a dynamic heterogeneous graph consisting of homogeneous subgraphs. The results show that DyHATR can be applied to identify link prediction on dynamic heterogeneous graphs by simultaneously capturing heterogeneous information and evolutionary patterns, and then considering them to carry out link predicition. Compared to the baseline method, the accuracy achieved by DyHATR is competitive, although the results can still be improved

Sentiment Analysis for Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection

Assessing transport accessibility for healthcare facility reconfiguration using GIS and multilevel modelling

Transport accessibility to healthcare facilities is a major issue in the United Kingdom, as recently demonstrated by the shift away from providing healthcare in acute hospitals to care closer to home . Common measures of accessibility focus on the creation of distance or travel time contours around a destination and devote less attention to individual differences such as user perceptions, their transport usage, and area-wide factors including income deprivation, safety and security. Failure to account for such factors may result in imperfect decision making in terms of healthcare relocation and reconfiguration. This thesis therefore aims to develop a user-based accessibility model by focusing on both individual socio-economic (e.g. age, gender, access to transport modes) and area-wide characteristics (e.g. income deprivation, public transport provision, safety and security). In order to identify important factors that affect accessibility and to develop the user-based accessibility model, two revealed preference questionnaire surveys were undertaken at Loughborough and Hinckley. The purpose of the first questionnaire was to understand underlying factors affecting accessibility to a healthcare facility. The results revealed that both individual and area-wide factors affect transport accessibility to a healthcare facility. The purpose of the second questionnaire was conducted to capture data relating to users perception of accessibility and their socio-economic factors so as to develop a user-perception based accessibility model. Network-based travel time and travel distance as well as public transport provision data from a respondent home to a healthcare facility were generated using a GIS technique. Individual-level questionnaire data were then integrated with the other secondary datasets (e.g. Census, Index of Multiple Deprivation, Accidents) using postcodes of survey respondents. Both single-level and multilevel mixed-effects linear regression models were employed to develop a relationship between user-perceptions relating to accessibility and the factors influencing accessibility. Multilevel models that can control data from the two levels (i.e. individuals nested within local areas) provided better goodness-of-fit statistics compared with those of single-level regression models. The results indicate that travel distance by car, number of available direct bus services, age, and destination choices affect user-perceptions of accessibility to a healthcare facility. For instance, if travel distance by car increases by one mile then the perception of accessibility to a healthcare facility decreases by four units (on a scale of 0-100). Surprisingly, many area-wide factors such as security and safety, income deprivation were found to be statistically insignificant. In order to see which healthcare facility is more accessible, calibrated multilevel models along with number of people within the catchment area were then employed to predict the overall accessibility score related to a healthcare facility. This is important for policy makers in healthcare facility relocation and reconfiguration with respect to user perception of transport accessibility. Also it would be valuable to organisations that need to make decisions based on their users perceptions who are the real decision makers as to whether to use a facility or not

Network representation learning: From traditional feature learning to deep learning

Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological information processing, and recommender systems. Deep Learning is a powerful tool to learn data features. However, it is non-trivial to generalize deep learning to graph-structured data since it is different from the regular data such as pictures having spatial information and sounds having temporal information. Recently, researchers proposed many deep learning-based methods in the area of NRL. In this survey, we investigate classical NRL from traditional feature learning method to the deep learning-based model, analyze relationships between them, and summarize the latest progress. Finally, we discuss open issues considering NRL and point out the future directions in this field. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study