With the rapid development of digital platforms, users can now interact in endless ways from writing business reviews and comments to sharing information with their friends and followers. As a result, organizations have numerous digital social networks available for graph learning problems with little guidance on how to select the right graph or how to combine multiple edge types. For example, while user-to-user interactions are directed in nature, many graph learning approaches use the undirected version of the network. In this paper, we introduce edge direction, edge weight, and multi-relational data for node prediction tasks. We first adapt an existing node attribute prediction method for binary prediction, LINK-Naive Bayes, to account for both edge direction and weights on single-layer networks. We compare predictive performance metrics across various node attribute prediction tasks for an ads click prediction task on Facebook and for a publicly available dataset from the Open Graph Benchmark (OGB). We observe meaningful predictive performance improvements when incorporating edge direction and weight, and performance that's competitive with the OGB Leaderboard. We then introduce an approach called MultiLayerLINK-NaiveBayes that can combine multiple network layers during training and observe superior performance over the single-layer results. Ultimately, whether edge direction, edge weights, and multi-layers are practically useful will depend on the particular setting. Our approach enables practitioners to quickly combine multiple layers and edge types.

Prior research has shown that TV content affects what people do on the web, particularly in the minutes after a TV ad airs, when online searches for the advertised product spike. To study this, researchers have typically focused on the total volume of search queries that include any and all keywords associated with the brands and products in TV ads. We argue that a granular consideration of search queries would be beneficial for two reasons. First, focusing on relevant keywords reduces measurement error, which can hinder identification of a significant effect from TV ads. Second, by grouping queries into themes based on semantic similarity, marketers can, for example, explore consumer intent in searches, or whether response manifests in queries with objective `value'. Our nuanced proposed approach considers query activity more broadly. Leveraging data from Bing and iSpotTV for 12 product campaigns, we first demonstrate that the outcome measure (i.e., response variable) can significantly influence conclusions about whether an ad has influenced search behavior. Second, we present a theme-based difference-in-differences method to determine the associations between TV ads and individual queries and query groups. We do this by first exploring the effects of TV ads on distinct, individual queries, and then aggregating queries into themes reflecting customer intentions, instead of aggregating all queries under a product or brand name umbrella. Our approach has implications for researchers who can improve measurement in search response; for marketers who can evaluate whether and how marketing messages resonate with consumers; and for sponsored search advertisers who can determine which keywords and queries they should bid on in the moments after their TV ads air. In addition, our method can be applied outside of the advertising context to understand how user generated content is impacted by events in general. Finally, we have developed a standalone tool that has been deployed to by Marketers internal to our company to generate reports to assess the success of their TV ad campaigns and has been deployed to our advertising customers by the Bing Ads Sales team to provide business intelligence11.

Due to climate changes and the uncertainties in future weather conditions, research on drought monitoring information received more attention from politicians and scientists. The objective of this paper is to develop a new intelligent system concept for drought information extraction and predictions from satellite images. For the modeling experiment, this study used 24 years of data sets on selected attributes. By using these data sets, ten models were developed for predicting DroughtObjects with a one- to four-month time lag for the growing season from June to October with an accuracy rate ranging from 0.71 to 0.95. The process of the system that uses the new concept was also demonstrated on an easy-to-use graphical user interface. The output of this new concept can be developed to a full system and is helpful for extracting the freely available satellite images for drought monitoring and climate change mitigation applications at different levels of decision making.

Microfinance Institutions are established with the mission and goals of serving poor people who lacked financial services from the conventional financial institutions but capable of engaging in small scale economic activities. These institutions are found to be quite relevant in the Ethiopian context and in their current state they are established very recently. To date, there are more than 20 recognized microfinance institutions in Ethiopia. Wisdom Microfinance is one of these accredited institutions. This study was aimed at exploring the potential application of data mining techniques for supporting key operational and strategic decisions of microfinance institutions. Qualitative and quantitative data were gathered and used in the study. The qualitative data revealed facts regarding the information requirement of the company and helped the investigator to understand the business rules. The quantitative data were used for conducting model building experiments. WEKA data mining software was used for model building and analysis. Three sets of classifier algorithms were used in the experiment. It was found out that J48 classifier delivered good result in terms of classifying instances correctly. The study recommends future researchers to experiment model building using multiple algorithms and larger datasets.

SUMMARY: We have developed medpie, a software package for preparing medical message board corpora and extracting patient mentions and statistics for drugs, herbs and adverse effects experienced from them. The package is divided into web-crawling, HTML-cleaning, de-identification and information extraction modules. It also includes a sample controlled vocabulary of drugs, herbs and adverse effect terms. AVAILABILITY: http://www.cis.upenn.edu/~ungar/medpie.zip. DEPENDENCIES: Python 2.6 or 2.7.

There are millions of public posts to medical message boards by users seeking support and information on a wide range of medical conditions. It has been shown that these posts can be used to gain a greater understanding of patients' experiences and concerns. As investigators continue to explore large corpora of medical discussion board data for research purposes, protecting the privacy of the members of these online communities becomes an important challenge that needs to be met. Extant entity recognition methods used for more structured text are not sufficient because message posts present additional challenges: the posts contain many typographical errors, larger variety of possible names, terms and abbreviations specific to Internet posts or a particular message board, and mentions of the authors' personal lives. The main contribution of this paper is a system to de-identify the authors of message board posts automatically, taking into account the aforementioned challenges. We demonstrate our system on two different message board corpora, one on breast cancer and another on arthritis. We show that our approach significantly outperforms other publicly available named entity recognition and de-identification systems, which have been tuned for more structured text like operative reports, pathology reports, discharge summaries, or newswire.

There are millions of public posts to medical message boards by users seeking support and information on a wide range of medical conditions. It has been shown that these posts can be used to gain a greater understanding of patients' experiences and concerns. As investigators continue to explore large corpora of medical discussion board data for research purposes, protecting the privacy of the members of these online communities becomes an important challenge that needs to be met. Extant entity recognition methods used for more structured text are not sufficient because message posts present additional challenges: the posts contain many typographical errors, larger variety of possible names, terms and abbreviations specific to Internet posts or a particular message board, and mentions of the authors' personal lives. The main contribution of this paper is a system to de-identify the authors of discussion board posts automatically, taking into account the aforementioned challenges. We demonstrate our system on two different message board corpora, one on breast cancer and another on arthritis. We show that our approach significantly outperforms other publicly available de-identification systems, which have been tuned for more structured text like operative reports, pathology reports and discharge summaries. Our software will be available for download as open source code in the near future.

A data mining (DM) process involves multiple stages. A simple, but typical, process might include preprocessing data, applying a data mining algorithm, and postprocessing the mining results. There are many possible choices for each stage, and only some combinations are valid. Because of the large space and nontrivial interactions, both novices and data mining specialists need assistance in composing and selecting DM processes. Extending notions developed for statistical expert systems we present a prototype intelligent discovery assistant (IDA), which provides users with 1) systematic enumerations of valid DM processes, in order that important, potentially fruitful options are not overlooked, and 2) effective rankings of these valid processes by different criteria, to facilitate the choice of DM processes to execute. We use the prototype to show that an IDA can indeed provide useful enumerations and effective rankings in the context of simple classification processes. We discuss how an IDA could be an important tool for knowledge sharing among a team of data miners. Finally, we illustrate the claims with a demonstration of cost-sensitive classification using a more complicated process and data from the 1998 KDDCUP competition.