The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a coherent platform to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles and methods for social media mining.

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This study aimed to evaluate the effects of dietary different antioxidants and plant oils on performance, apparent metabolizable energy and protein digestibility, meat quality and meat fatty acid composition of broiler chickens. In all, 480 male broiler chicks of 1‐day old were assigned in a completely randomized design with factorial arrangement 2 × 5 (plant oil sources [soybean and rapeseed oils] and antioxidant sources [vitamin E, Thyme, Rosemary and Satureja essential oils] furthermore control treatment without antioxidant). The results indicated that at 1–42 d of age, growth performance and carcass yield of birds were not influenced by dietary plant oils and antioxidant supplementations. Dietary Thyme essential oil (300 mg/kg) resulted in an increase in crude protein digestibility and birds fed on diets without antioxidant showed increase in the apparent metabolizable energy (p < .01). Birds receiving the combination of soybean oil with Rosemary essential oil had lowest malondialdehyde concentration in comparison to birds receiving other treatments (p < .05) in the drumstick meat. Also, birds receiving the combination of soybean oil with vitamin E had lowest malondialdehyde concentration in comparison to birds receiving other treatments (p < .05) in the breast meat. The results indicated that treatments did not influence water holding capacity of meat. Also, dietary rapeseed oil and Thyme essential oil supplementations, separately, decreased saturated fatty acid (p < .01) and increased unsaturated fatty acid and unsaturated to saturated fatty acids ratio (p < .01) of drumstick meat tissue in broiler chicken (p < .01). In conclusion, dietary rapeseed oil and Thyme essential oil increased in n‐3 polyunsaturated fatty acids in the drumstick meat (p < .01) and a combination of dietary soybean oil, Rosemary essential oil and vitamin E decreased the lipid oxidation in the meat of broiler chickens (p < .05).

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Users' personal information such as their political views is important for many applications such as targeted advertisements or realtime monitoring of political opinions. Huge amounts of data generated by social media users present opportunities and challenges to study these preferences in a large scale. In this paper, we aim to infer social media users' political views when only network information is available. In particular, given personal preferences about some of the social media users, how can we infer the preferences of unobserved individuals in the same network? There are many existing solutions that address the problem of classification with networked data problem. However, networks in social media normally involve millions and even hundreds of millions of nodes, which make the scalability an important problem in inferring personal preferences in social media. To address the scalability issue, we use social influence theory to construct new features based on a combination of local and global structures of the network. Then we use these features to train classifiers and predict users' preferences. Due to the size of real-world social networks, using the entire network information is inefficient and not practical in many cases. By extracting local social dimensions, we present an efficient and scalable solution. Further, by capturing the network's global pattern, the proposed solution, balances the performance requirement between accuracy and efficiency.

ObjectivesPrediabetes is a major epidemic and is associated with adverse cardio-cerebrovascular outcomes. Early identification of patients who will develop rapid progression of atherosclerosis could be beneficial for improved risk stratification. In this paper, we investigate important factors impacting the prediction, using several machine learning methods, of rapid progression of carotid intima-media thickness in impaired glucose tolerance (IGT) participants.MethodsIn the Actos Now for Prevention of Diabetes (ACT NOW) study, 382 participants with IGT underwent carotid intima-media thickness (CIMT) ultrasound evaluation at baseline and at 15–18 months, and were divided into rapid progressors (RP, n = 39, 58 ± 17.5 μM change) and non-rapid progressors (NRP, n = 343, 5.8 ± 20 μM change, p < 0.001 versus RP). To deal with complex multi-modal data consisting of demographic, clinical, and laboratory variables, we propose a general data-driven framework to investigate the ACT NOW dataset. In particular, we first employed a Fisher Score-based feature selection method to identify the most effective variables and then proposed a probabilistic Bayes-based learning method for the prediction. Comparison of the methods and factors was conducted using area under the receiver operating characteristic curve (AUC) analyses and Brier score.ResultsThe experimental results show that the proposed learning methods performed well in identifying or predicting RP. Among the methods, the performance of Naïve Bayes was the best (AUC 0.797, Brier score 0.085) compared to multilayer perceptron (0.729, 0.086) and random forest (0.642, 0.10). The results also show that feature selection has a significant positive impact on the data prediction performance.ConclusionsBy dealing with multi-modal data, the proposed learning methods show effectiveness in predicting prediabetics at risk for rapid atherosclerosis progression. The proposed framework demonstrated utility in outcome prediction in a typical multidimensional clinical dataset with a relatively small number of subjects, extending the potential utility of machine learning approaches beyond extremely large-scale datasets.