Predicting Adoption Probabilities in Social Networks

Abstract

In a social network, adoption probability refers to the probability that a social entity will adopt a product, service, or opinion in the foreseeable future. Such probabilities are central to fundamental issues in social network analysis, including the influence maximization problem. In practice, adoption probabilities have significant implications for applications ranging from social network-based target marketing to political campaigns; yet, predicting adoption probabilities has not received sufficient research attention. Building on relevant social network theories, we identify and operationalize key factors that affect adoption decisions: social influence, structural equivalence, entity similarity, and confounding factors. We then develop the locally-weighted expectation-maximization method for Naïve Bayesian learning to predict adoption probabilities on the basis of these factors. The principal challenge addressed in this study is how to predict adoption probabilities in the presence of confounding factors that are generally unobserved. Using data from two large-scale social networks, we demonstrate the effectiveness of the proposed method. The empirical results also suggest that cascade methods primarily using social influence to predict adoption probabilities offer limited predictive power, and that confounding factors are critical to adoption probability predictions.

Solusi Pengembangan Umkm Melalui Ontologi

“…Beberapa permasalahan baik yang bersumber dari aspek internal maupun eksternal yang sering dijumpai UMKM. Permasalahan dari aspek internal diantaranya: kualitas sumber daya manusia rendah, rendah dalam pemanfaatan teknologi, keterbatasan dalam akses informasi, daya beli bahan baku rendah (Ramdhansyah & Silalahi 2013) (Glisovic & Martinez 2012) (Komarudin 2014) (Fang et al 2013) (Suandi & Susilo 2011). Permasalahan karena aspek eksternal diantaranya kesulitan dalam mendapatkan sumber pendanaan terutama dari perbankan, iklim usaha yang tidak menunjang, tidak mampu menembus pasar ekspor, ancaman pasar global (Suyatna n.d.), kurangnya dukungan pihak eksternal dalam mendukung UMKM, kebijakan pemerintah kurang kondusif (Suandi & Susilo 2011).…”

Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers

“…Deal with labelled data (Su, Shirab, & Matwin, 2011;Bhattu & Somayajulu, 2012). c. Adopt probability value (Fang, Hu, Li, & Tsai, 2013).…”

Product Adoption Rate Prediction: A Multi-factor View

“…In the literature, many efforts have been devoted to the product adoption prediction problem, where the product could be a particular brand, a technology service or an opinion [10,23,8]. These works usually classified users in two categories, the adopters that have already consumed this product and the non-adopters that have not consumed it till now.…”

“…Unfortunately, none of the existing models (e.g., the time-series forecasting models [3] and the hidden markov model [18]) can be applied directly due to the unique characteristics of this problem. First, a user's decision making process is very complex as many factors around her may contribute to the final decision, e.g., the users' own profiles [23], the social network structure [8] and the overall crowd awareness [20]. How to design a flexible prediction model that can leverage many different factors remains pretty much open.…”

“…As a trend, leveraging these series for future adoption prediction has attracted increasing attention from both academy and industry [12,5]. Accurate prediction not only helps to understand the human decision process, but is also crucial to a wide range of business applications ranging from personalized recommendation [2], targeted marketing [4] to customer churn prediction [22].In the literature, many efforts have been devoted to the product adoption prediction problem, where the product could be a particular brand, a technology service or an opinion [10,23,8]. These works usually classified users in two categories, the adopters that have already consumed this product and the non-adopters that have not consumed it till now.…”