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Fake tweets are observed to be ever-increasing, demanding immediate countermeasures to combat their spread. During COVID-19, tweets with misinformation should be flagged and neutralized in their early stages to mitigate the damages. Most of the existing methods for early detection of fake news assume to have enough propagation information for large labeled tweets -- which may not be an ideal setting for cases like COVID-19 where both aspects are largely absent. In this work, we present ENDEMIC, a novel early detection model which leverages exogenous and endogenous signals related to tweets, while learning on limited labeled data. We first develop a novel dataset, called CTF for early COVID-19 Twitter fake news, with additional behavioral test sets to validate early detection. We build a heterogeneous graph with follower-followee, user-tweet, and tweet-retweet connections and train a graph embedding model to aggregate propagation information. Graph embeddings and contextual features constitute endogenous, while time-relative web-scraped information constitutes exogenous signals. ENDEMIC is trained in a semi-supervised fashion, overcoming the challenge of limited labeled data. We propose a co-attention mechanism to fuse signal representations optimally. Experimental results on ECTF, PolitiFact, and GossipCop show that ENDEMIC is highly reliable in detecting early fake tweets, outperforming nine state-of-the-art methods significantly., Comment: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2021

Short text is a popular avenue of sharing feedback, opinions and reviews on social media, e-commerce platforms, etc. Many companies need to extract meaningful information (which may include thematic content as well as semantic polarity) out of such short texts to understand users' behaviour. However, obtaining high quality sentiment-associated and human interpretable themes still remains a challenge for short texts. In this paper we develop ELJST, an embedding enhanced generative joint sentiment-topic model that can discover more coherent and diverse topics from short texts. It uses Markov Random Field Regularizer that can be seen as a generalisation of skip-gram based models. Further, it can leverage higher-order semantic information appearing in word embedding, such as self-attention weights in graphical models. Our results show an average improvement of 10% in topic coherence and 5% in topic diversification over baselines. Finally, ELJST helps understand users' behaviour at more granular levels which can be explained. All these can bring significant values to the service and healthcare industries often dealing with customers., Comment: Accepted in International AAAI Conference on Web and Social Media (ICWSM), 2021

As the COVID-19 pandemic sweeps across the world, it has been accompanied by a tsunami of fake news and misinformation on social media. At the time when reliable information is vital for public health and safety, COVID-19 related fake news has been spreading even faster than the facts. During times such as the COVID-19 pandemic, fake news can not only cause intellectual confusion but can also place lives of people at risk. This calls for an immediate need to contain the spread of such misinformation on social media. We introduce CTF, the first COVID-19 Twitter fake news dataset with labeled genuine and fake tweets. Additionally, we propose Cross-SEAN, a cross-stitch based semi-supervised end-to-end neural attention model, which leverages the large amount of unlabelled data. Cross-SEAN partially generalises to emerging fake news as it learns from relevant external knowledge. We compare Cross-SEAN with seven state-of-the-art fake news detection methods. We observe that it achieves $0.95$ F1 Score on CTF, outperforming the best baseline by $9\%$. We also develop Chrome-SEAN, a Cross-SEAN based chrome extension for real-time detection of fake tweets., Comment: The Journal of Applied Soft Computing

Online social media platforms have made the world more connected than ever before, thereby making it easier for everyone to spread their content across a wide variety of audiences. Twitter is one such popular platform where people publish tweets to spread their messages to everyone. Twitter allows users to Retweet other users' tweets in order to broadcast it to their network. The more retweets a particular tweet gets, the faster it spreads. This creates incentives for people to obtain artificial growth in the reach of their tweets by using certain blackmarket services to gain inorganic appraisals for their content. In this paper, we attempt to detect such tweets that have been posted on these blackmarket services in order to gain artificially boosted retweets. We use a multitask learning framework to leverage soft parameter sharing between a classification and a regression based task on separate inputs. This allows us to effectively detect tweets that have been posted to these blackmarket services, achieving an F1-score of 0.89 when classifying tweets as blackmarket or genuine., Comment: 4 pages, IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM) 2019

The effects of pluralism on today's college campuses are intense. The place held by religion in this context is often awkward, but as a dimension of diversity and a potent force in human interaction, it deserves the same tolerance as any other aspect of pluralism and should not be trivialized. (MSE)

What is perceived as poor fit between liberal arts and the individual disciplines results not from narrow subject matter or analytical focus but from the disciplines keeping too many of the interesting questions to themselves. College students are not exposed enough to the reasons their teachers find their fields compelling. (MSE)

Two-phase gas-liquid flows are expected to occur in many future space operations. Due to a lack of buoyancy in the microgravity environment, two-phase flows are known to behave differently than those in earth gravity. Despite these concerns, little research has been conducted on microgravity two-phase flow and the current understanding is poor. This dissertation describes an experimental and modeling study of the characteristics of two-phase flows in microgravity. An experiment was operated onboard NASA aircraft capable of producing short periods of microgravity. In addition to high speed photographs of the flows, electronic measurements of void fraction, liquid film thickness, bubble and wave velocity, pressure drop and wall shear stress were made for a wide range of liquid and gas flow rates. The effects of liquid viscosity, surface tension and tube diameter on the behavior of these flows were also assessed. From the data collected, maps showing the occurrence of various flow patterns as a function of gas and liquid flow rates were constructed. Earth gravity two-phase flow models were compared to the results of the microgravity experiments and in some cases modified. Models were developed to predict the transitions on the flow pattern maps. Three flow patterns, bubble, slug and annular flow, were observed in microgravity. These patterns were found to occur in distinct regions of the gas-liquid flow rate parameter space. The effect of liquid viscosity, surface tension and tube diameter on the location of the boundaries of these regions was small. Void fraction and Weber number transition criteria both produced reasonable transition models. Void fraction and bubble velocity for bubble and slug flows were found to be well described by the Drift-Flux model used to describe such flows in earth gravity. Pressure drop modeling by the homogeneous flow model was inconclusive for bubble and slug flows. Annular flows were found to be complex systems of ring-like waves and a substrate film. Pressure drop was best fitted with the Lockhart- Martinelli model. Force balances suggest that droplet entrainment may be a large component of the total pressure drop.