## 2 Summary of the Contributions
### Social Media  
One prominent dark side of online information behavior is the spreading of rumors on social media. Paper [13] analyses the association between user features and rumor refuting behavior in different rumor categories. Natural language processing (NLP) techniques are applied to quantify the user's sentiment tendency and recent interests. The users' personalized features are used to train XGBoost classification model to identify potential refuters. The results revealed that there are significant differences between rumor stiflers and refuters, as well as between refuters for different categories.  
The objective of [14] is to detect variables that allow organizations to manage their social network services efficiently. This study, applying ML algorithms and multiple linear regression, reveals which aspects of published content increase the recognition of publications through retweets and favorites. The findings of this research provide new knowledge about trends and patterns of use in social media, providing academics and professionals with the necessary guidelines to efficiently manage these technologies in the organizational field.  
Paper [15] concerns the tourists' sentiments regarding travel destinations based on online travel review texts. The authors transformed sentiment analysis into a multi-classification problem based on ML methods, and further designed a keyword semantic expansion method based on a knowledge graph. The method extracts keywords from online travel review texts and obtains the concept list of keywords through the knowledge graph. This list is then added to the review text to facilitate the construction of semantically expanded classification data. The results of sentiment analysis form an important basis for tourism decision making.  
Micro-blogs, such as Twitter, have become important tools to share opinions and information among users. The authors of [16] wonder how a user can discover influencers concerned with their interest. They propose a classification model trained on messages labeled with topical classes, so as this model is able to classify unlabeled messages. This model can be used to reveal the hidden topic the messages are talking about.  
With the widespread use of over-the-top (OTT) media, such as YouTube and Netflix, network markets are changing and innovating rapidly, making it essential for network providers to quickly and efficiently analyze OTT traffic with respect to pricing plans and infrastructure investments. In [17], a time-aware deep learning method of analyzing OTT traffic to classify users for this purpose is presented. A novel framework to better exploit accuracy, while dramatically reducing classification time is proposed. The resultant approach provides a simple method for customizing pricing plans and load balancing by classifying OTT users more accurately.  
Recommendation systems aim to decipher user interests, preferences, and behavioral patterns automatically. The credibility of the recommendation is of magnificent importance in crowdfunding project recommendations. Paper [18] devises a hybrid ML-based approach for credible crowdfunding projects' recommendations by wisely incorporating backers' sentiments and other influential features. The proposed model has four modules: a feature extraction module, a hybrid latent Dirichlet allocation and LSTM-based latent topics evaluation module, credibility formulation, and recommendation module. The proposed model's evaluation depicts that credibility assessment based on the hybrid ML approach contributes more efficient results than existing recommendation models.