Since most machine learning models provide no explanations for the predictions, their predictions are obscure for the human. The ability to explain a model's prediction has become a necessity in many applications including Twitter mining. In this work, we propose a method called Explainable Twitter Mining (Ex-Twit) combining Topic Modeling and Local Interpretable Model-agnostic Explanation (LIME) to predict the topic and explain the model predictions. We demonstrate the effectiveness of Ex-Twit on Twitter health-related data.
Use of data generated through social media is gradually increasing. Several works have been done on prediction of social media content @cite_20 ; @cite_7 ; @cite_24 ; @cite_6 ; @cite_17 ; @cite_11 ; @cite_22 ; @cite_3 . Social media support the collection and analysis of data in real time in the real world.
@cite_11 @cite_22 @cite_7 @cite_6 @cite_3 @cite_24 @cite_20 @cite_17
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PHEME attempts to identify four kinds of false claim in social media and on the web, in real time: rumours, disinformation, misinformation and speculation. This brings challenges in modelling the behaviour of individual users, networks of users and information diffusion. This presentation proposal discusses the issues addressed by the project and the challenges it faces, in this emerging and rapidly-developing domain. Social media content can be used as a complementary source to the traditional methods for extracting and studying collective social attributes. This study focuses on the prediction of the occupational class for a public user profile. Our analysis is conducted on a new annotated corpus of Twitter users, their respective job titles, posted textual content and platform-related attributes. We frame our task as classification using latent feature representations such as word clusters and embeddings. The employed linear and, especially, non-linear methods can predict a user’s occupational class with strong accuracy for the coarsest level of a standard occupation taxonomy which includes nine classes. Combined with a qualitative assessment, the derived results confirm the feasibility of our approach in inferring a new user attribute that can be embedded in a multitude of downstream applications. In recent years, there has been a growing trend to use publicly available social media sources within the field of journalism. Breaking news has tight reporting deadlines, measured in minutes not days, but content must still be checked and rumors verified. As such, journalists are looking at automated content analysis to prefilter large volumes of social media content prior to manual verification. This article describes a real-time social media analytics framework for journalists. We extend our previously published geoparsing approach to improve its scalability and efficiency. We develop and evaluate a novel approach to geosemantic feature extraction, classifying evidence in terms of situatedness, timeliness, confirmation, and validity. Our approach works for new unseen news topics. We report results from four experiments using five Twitter datasets crawled during different English-language news events. One of our datasets is the standard TREC 2012 microblog corpus. Our classification results are promising, with F1 scores varying by class from 0.64 to 0.92 for unseen event types. We lastly report results from two case studies during real-world news stories, showcasing different ways our system can assist journalists filter and cross-check content as they examine the trust and veracity of content and sources. Purpose – Social media provide an impressive amount of data about users and their interactions, thereby offering computer and social scientists, economists, and statisticians – among others – new opportunities for research. Arguably, one of the most interesting lines of work is that of predicting future events and developments from social media data. However, current work is fragmented and lacks of widely accepted evaluation approaches. Moreover, since the first techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Therefore, better understanding the predictive power and limitations of social media is of utmost importance. Design methodology approach – Different types of forecasting models and their adaptation to the special circumstances of social media are analyzed and the most representative research conducted up to date is surveyed. Presentations of current research on techniques, methods, and empirical studies ai...