Assessing Trust and Veracity of Data in Social Media

Social media highly impacts our knowledge and perception of the world. With the tremendous amount of data that is circulating in social media and initiated by a vast number of users from all over the world, extracting useful information from such data and assessing its veracity has become much more challenging. Data veracity refers to the trustworthiness and certainty of data. The challenges of handling textual data in social media have raised the need for efficient tools to extract, understand, and assess the veracity of information circulating in social media at a given time. In this thesis, we present three research problems to address major challenges of handling textual data in social media. First, overwhelming the user with huge volumes of short, noisy, and unstructured textual data complicates the task of understanding what topics are discussed by users in micro-blogging websites. Topic models were proposed to automatically learn a set of keywords that better describe each topic covered by a large corpus of text documents to enable fast and effective browsing and exploration of its contents. However, in order for the results of topic modeling algorithms to be useful, these results have to be interpretable. Applying topic models to social media data to get meaningful results is not a trivial task. In this thesis, we study the problem of improving interpretation of topic modeling of micro-posts in social media. We propose a new method that incorporates topic modeling, a lexical database, and the set of hashtags available in the corpus of micro-posts to produce a higher quality representation of each extracted topic. Extensive experiments on two real-life datasets collected from Twitter show that our method outperforms the state-of-the-art model in terms of perplexity, topics' coherence, and their quality. Second, the nature and flexibility of social media facilitate the process of posting unverified information, especially during the rapid diffusion of breaking news. Efficiently detecting and acting upon unverified breaking news rumors throughout social media is of high importance to minimizing their harmful effect. However, detecting them is not a trivial task. They belong to unseen topics or events that are not covered in the training dataset. In this thesis, we study the problem of assessing the veracity of information contained in micro-posts regarding emerging stories and topics of breaking news. We propose a new approach that jointly learns word embeddings and trains a neural network model with two different objectives to automatically identify unverified micro-posts spreading in social media during breaking news. Extensive experiments on real-life datasets show that our proposed model outperforms the state-of-the-art classifier as well as other baseline classifiers in terms of precision, recall, and F1. Finally, the uncertainty and chaos associated with hot and sensitive breaking news and emergencies facilitate the explosive spread of high-engaging breaking news rumors that might be extremely damaging. In such a case, authorities have to prioritize the rumors verification process and act upon high-engaging breaking news rumors quickly to reduce their damaging consequences. However, this is an extremely challenging task. In this thesis, we study the problem of identifying rumors micro-posts that are most likely to become viral and achieve high engagement rates among recipients in social media during breaking news. We propose a multi-task neural network to jointly learn the two tasks of breaking news rumors detection and breaking news rumors popularity prediction. Extensive experiments on real-life datasets show that the performance of our joint learning model outperforms other baseline classifiers in terms of precision, recall, and F1 and is capable of identifying high-engaging breaking news rumors with high accuracy

Managing buyer experience in a buyer–supplier relationship in MSMEs and SMEs

Monitoring buyer experience provides competitive advantages for suppliers as buyers explore the market before reaching a salesperson. Still, not many B2B suppliers monitor their buyers’ expectations throughout their procurement journey, especially in MSMEs and SMEs. In addition, the inductive research on evaluating buyer experience in buyer–supplier relationships is minimal, leaving an unexplored research area. This study explores antecedents of buyer experience during the buyer–supplier relationship in MSMEs and SMEs. Further, we investigate the nature of the influence of extracted precursors on the buyer experience. Firstly, we obtain the possible antecedents from the literature on buyer–supplier experience and supplier selection criteria. We also establish hypotheses based on transaction cost theory, resource-based view (RBV), and information processing view. Secondly, we employ an investigation based on the social media analytics-based approach to uncover the antecedents of buyer experience and their nature of influence on MSMEs and SME suppliers. We found that buyer experience is influenced by sustainable orientation, management capabilities (such as crisis management and process innovation), and suppliers’ technology capabilities (digital readiness, big data analytical capability)