Despite the growing body of research tackling offensive language in social media, this research is predominantly reactive, determining if content already posted in social media is abusive. There is a gap in predictive approaches, which we address in our study by enabling to predict the volume of abusive replies a tweet will receive after being posted. We formulate the problem from the perspective of a social media user asking: ``if I post a certain message on social media, is it possible to predict the volume of abusive replies it might receive?'' We look at four types of features, namely text, text metadata, tweet metadata, and account features, which also help us understand the extent to which the user or the content helps predict the number of abusive replies. This, in turn, helps us develop a model to support social media users in finding the best way to post content. One of our objectives is also to determine the extent to which the volume of abusive replies that a tweet will get are motivated by the content of the tweet or by the identity of the user posting it. Our study finds that one can build a model that performs competitively by developing a comprehensive set of features derived from the content of the message that is going to be posted. In addition, our study suggests that features derived from the user's identity do not impact model performance, hence suggesting that it is especially the content of a post that triggers abusive replies rather than who the user is.

If you spend any time on the internet, you're likely now familiar with the gray-and-teal screenshots of AI-generated text. At first they were meant to illustrate ChatGPT's surprising competence at generating human-sounding prose, and then to demonstrate the occasionally unsettling answers that emerged once the general public could bombard it with prompts. OpenAI, the organization that is developing the tool, describes one of its biggest problems this way: "ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers." In layman's terms, the chatbot makes stuff up. As similar services, such as Google's Bard, have rushed their tools into public testing, their screenshots have demonstrated the same capacity for fabricating people, historical events, research citations, and more, and for rendering those falsehoods in the same confident, tidy prose.

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We propose an online scheduling algorithm for posting crowdsourcing tasks which maximizes a novel metric called task viewership. This metric is computed using stochastic model based on coverage process and it measures the likelihood that a task is viewed by multiple crowd workers, which is correlated to the likelihood that it will be selected and completed.