A Personalized Travel Recommendation System Using Social Media Analysis

Personalization of recommender systems enables customized services to users. Social media is one resource that aids personalization. This study explores the use of twitter data to personalize travel recommendations. A machine learning classification model is used to identify travel related tweets. The travel tweets are then used to personalize recommendations regarding places of interest for the user. Places of interest are categorized as: historical buildings, museums, parks, and restaurants. To better personalize the model, travel tweets of the user\u27s friends and followers are also mined. Volunteer twitter users were asked to provide their twitter handle as well as rank their travel category preferences in a survey. We evaluated our model by comparing the predictions made by our model with the users choices in the survey. The evaluations show 68% prediction accuracy. The accuracy can be improved with a better travel-tweet training dataset as well as a better travel category identification technique using machine learning. The travel categories can be increased to include items like sports venues, musical events, entertainment, etc. and thereby fine-tune the recommendations. The proposed model lists \u27n\u27 places of interest from each category in proportion to the travel category score generated by the model

A comparison of patient testimonials on YouTube of the most common orthodontic treatment modalities: braces, in-office aligners, and direct-to-consumer aligners

Introduction: The objectives of this research was to investigate and compare the educational value of the most popular YouTube orthodontic patient testimonials between braces (B), in- office aligners (IOA), and direct-to-consumer aligners (DTCA), and to classify the emotional response of the viewers through a sentiment analysis of the video comments. Methods: Three different phrases relevant to B, IOA, and DTCA were searched on YouTube. The 20 most popular patient testimonial videos that met the criteria for each group were selected, for a total of 60 videos. Using the YouTube API for each video, 13 video metrics were extracted, an information completeness score (ICS) was assigned, and an analysis of the video comments was performed using sentiment analysis software. Results: The 60 videos included in this study were viewed 34,384,786 times by internet users. Braces videos have significantly more likes, comments, and a higher viewer interaction score than the IOA and DTCA videos. IOA videos had a higher median ICS than B and DTCA videos. Of the 5149 video comments with polarity, 53.6% were positive and 46.4% were negative (P Conclusions: There is high user engagement on YouTube with orthodontic patient testimonials. YouTube users interact with braces patient testimonials the most. YouTube viewers’ comments on orthodontic patient testimonials express more positive sentiment than negative sentiment. There is no significant difference in positive and negative sentiment between the video comments for the three different treatment modalities

Social Emotion Mining Techniques for Facebook Posts Reaction Prediction

As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called `reactions'. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages of firms/companies (like supermarket chains). For this purpose, we collected posts (and their reactions) from Facebook pages of large supermarket chains and constructed a dataset which is available for other researches. In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. Results of the neural networks were improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post. The final model (a combination of neural network and a baseline emotion miner) is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.135.Comment: 10 pages, 13 figures and accepted at ICAART 2018. (Dataset: https://github.com/jerryspan/FacebookR

Debunking in a World of Tribes

Recently a simple military exercise on the Internet was perceived as the beginning of a new civil war in the US. Social media aggregate people around common interests eliciting a collective framing of narratives and worldviews. However, the wide availability of user-provided content and the direct path between producers and consumers of information often foster confusion about causations, encouraging mistrust, rumors, and even conspiracy thinking. In order to contrast such a trend attempts to \textit{debunk} are often undertaken. Here, we examine the effectiveness of debunking through a quantitative analysis of 54 million users over a time span of five years (Jan 2010, Dec 2014). In particular, we compare how users interact with proven (scientific) and unsubstantiated (conspiracy-like) information on Facebook in the US. Our findings confirm the existence of echo chambers where users interact primarily with either conspiracy-like or scientific pages. Both groups interact similarly with the information within their echo chamber. We examine 47,780 debunking posts and find that attempts at debunking are largely ineffective. For one, only a small fraction of usual consumers of unsubstantiated information interact with the posts. Furthermore, we show that those few are often the most committed conspiracy users and rather than internalizing debunking information, they often react to it negatively. Indeed, after interacting with debunking posts, users retain, or even increase, their engagement within the conspiracy echo chamber