Measuring Social Influence in Online Social Networks - Focus on Human Behavior Analytics

With the advent of online social networks (OSN) and their ever-expanding reach, researchers seek to determine a social media user’s social influence (SI) proficiency. Despite its exploding application across multiple domains, the research confronts unprecedented practical challenges due to a lack of systematic examination of human behavior characteristics that impart social influence. This work aims to give a methodical overview by conducting a targeted literature analysis to appraise the accuracy and usefulness of past publications. The finding suggests that first, it is necessary to incorporate behavior analytics into statistical measurement models. Second, there is a severe imbalance between the abundance of theoretical research and the scarcity of empirical work to underpin the collective psychological theories to macro-level predictions. Thirdly, it is crucial to incorporate human sentiments and emotions into any measure of SI, particularly as OSN has endowed everyone with the intrinsic ability to influence others. The paper also suggests the merits of three primary research horizons for future considerations

On Growing Better Decision Trees from Data

This thesis investigates the problem of growing decision trees from data, for the purposes of classification and prediction. After a comprehensive, multi-disciplinary survey of work on decision trees, some algorithmic extensions to existing tree growing methods are considered. The implications of using (1) less greedy search and (2) less restricted splits at tree nodes are systematically studied. Extending the traditional axis-parallel splits to oblique splits is shown to be practical and beneficial for a variety of problems. However, the use of more extensive search heuristics than the traditional greedy heuristic is argued to be unnecessary, and often harmful. Any effort to build good decision trees from real-world data involves "massaging " the data into a suitable form. Two forms of data massaging, domain-independent and domain-specific, are distinguished in this work. A new framework is outlined for the former, and the importance of the latter is illustrated in the context of two ..

On growing better decision trees from data

132 tr. ; 30 cm

On growing better decision trees from data

ix, 156 tr. ; 30 cm