Predicting User Interaction on Social Media using Machine Learning

Analysis of Facebook posts provides helpful information for users on social media. Current papers about user engagement on social media explore methods for predicting user engagement. These analyses of Facebook posts have included text and image analysis. Yet, the studies have not incorporate both text and image data. This research explores the usefulness of incorporating image and text data to predict user engagement. The study incorporates five types of machine learning models: text-based Neural Networks (NN), image-based Convolutional Neural Networks (CNN), Word2Vec, decision trees, and a combination of text-based NN and image-based CNN. The models are unique in their use of the data. The research collects 350k Facebook posts. The models learn and test on advertisement posts in order to predict user engagement. User engagements includes share count, comment count, and comment sentiment. The study found that combining image and text data produced the best models. The research further demonstrates that combined models outperform random models

DeepCity: A Feature Learning Framework for Mining Location Check-ins

Online social networks being extended to geographical space has resulted in large amount of user check-in data. Understanding check-ins can help to build appealing applications, such as location recommendation. In this paper, we propose DeepCity, a feature learning framework based on deep learning, to profile users and locations, with respect to user demographic and location category prediction. Both of the predictions are essential for social network companies to increase user engagement. The key contribution of DeepCity is the proposal of task-specific random walk which uses the location and user properties to guide the feature learning to be specific to each prediction task. Experiments conducted on 42M check-ins in three cities collected from Instagram have shown that DeepCity achieves a superior performance and outperforms other baseline models significantly

Regression and Learning to Rank Aggregation for User Engagement Evaluation

User engagement refers to the amount of interaction an instance (e.g., tweet, news, and forum post) achieves. Ranking the items in social media websites based on the amount of user participation in them, can be used in different applications, such as recommender systems. In this paper, we consider a tweet containing a rating for a movie as an instance and focus on ranking the instances of each user based on their engagement, i.e., the total number of retweets and favorites it will gain. For this task, we define several features which can be extracted from the meta-data of each tweet. The features are partitioned into three categories: user-based, movie-based, and tweet-based. We show that in order to obtain good results, features from all categories should be considered. We exploit regression and learning to rank methods to rank the tweets and propose to aggregate the results of regression and learning to rank methods to achieve better performance. We have run our experiments on an extended version of MovieTweeting dataset provided by ACM RecSys Challenge 2014. The results show that learning to rank approach outperforms most of the regression models and the combination can improve the performance significantly.Comment: In Proceedings of the 2014 ACM Recommender Systems Challenge, RecSysChallenge '1

The use of social media for improving energy consumption awareness and efficiency: An overview of existing tools

Raising consumers’ awareness of energy consumption is one of the first steps in encouraging the adoption of energy saving behaviours that result in energy efficiency. Green information systems are becoming recognised as a solution to many environmental problems although information technology (e.g. disposal of IT devices) has also been associated with causing detrimental effects on the environment. Researchers and practitioners have begun to focus on Green ICT but there is little scholarly research on the use of ICT tools such as social media from an energy efficiency context to raise consumer awareness and improve their engagement in tackling environmental issues. Therefore, the aim of this paper is to explore the use of social media and existing tools for the interaction of people on energy saving discussions and for generating awareness and engagement (which thereby leads to energy efficiency behaviour). In this paper the authors provide a state of the art review around the concept of energy awareness, models of consumer engagement, and more importantly the use of social media in the energy efficiency context. This research is based on a desk-based normative review and seeks to provide a better understanding to both scholars and practitioners involved in the use of ICT for driving energy consumer awareness and engagement for energy efficiency.This work evolved in the context of the project DAREED (Decision support Advisor for innovative business models and useR engagement for smart Energy Efficient Districts), www.dareed.eu, a project co-funded by the EC within FP7, Grant agreement no: 609082