Using word embeddings in Twitter election classification

Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, as well as the parameters of the word embedding training process, namely the context window size, the dimensionality and the number of negative samples, on the attained classification performance. By comparing the classification results of word embedding models that have been trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data should align with the Twitter classification dataset both in data type and time period to achieve significantly better performance compared to baselines such as SVM with TF-IDF. Moreover, by evaluating the results of word embedding models trained using various context window sizes and dimensionalities, we find that large context window and dimension sizes are preferable to improve the performance. However, the number of negative samples parameter does not significantly affect the performance of the CNN classifiers. Our experimental results also show that choosing the correct word embedding model for use with CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings. Finally, for out-of-vocabulary (OOV) words that are not available in the learned word embedding models, we show that a simple OOV strategy to randomly initialise the OOV words without any prior knowledge is sufficient to attain a good classification performance among the current OOV strategies (e.g. a random initialisation using statistics of the pre-trained word embedding models).     

Teaching PEACE: A plan for effective crisis communication instruction

Courses: This unit activity is intended for public relations (PR), crisis communication, or journalism courses.

Objectives: The purpose is to equip future PR professionals with critical thinking skills and experience to manage crises. Students demonstrate mastery in two ways: by crafting clear crisis response messages and materials in a narrow time frame, and by applying a crisis communication heuristic to manage a simulated crisis event.     

Investigating the quality of interactions and public engagement around scientific papers on Twitter

This study explores science communication on Twitter by investigating a sample of tweets referring to academic papers in five different scientific fields. The specifications of science communicators on Twitter, the characteristics of those who initiate actions (by tweeting), the extent and quality of reactions (retweeting), individual and group interactions, and the distribution of tweets across types of engagement in the process of science communication (i.e., dissemination, consultation, and evaluation) were explored. A broad array of actors is involved in the communication of science on Twitter, with individual citizens and individual researchers playing an important role. In principle, this is promising for creating direct interaction, which can be difficult through more traditional mass media. The vast majority of communication activities regarding academic papers is undigested dissemination with almost no sign of debate, contestation, or collective reflection. Another general finding of this study is that bot accounts play a major role in the science communication landscape on Twitter.