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The current era of advanced computational mobile systems, continuous connectivity and multi-variate data has led to the deployment of rich information settings that generate constant and close to real-time feedback. Journalists and authors of articles in the area of Data Journalism have only recently acknowledged the influence that the audience reactions and opinions can bring to effective writing, so to be widely appreciated. Such feedback may be obtained using specific metrics that describe the user behavior during the interaction process like shares, comments, likes, claps, recommendations, or even with the use of specialized mechanisms like mood meters that display certain emotions of readers they experience while reading a story. However, which characteristics can reveal an article's character or type in relation to the collected data and the audience reflection to the benefit of the author? In this paper, we investigate the relationships between the characteristics of an article like structure, style of speech, sentiment, author's popularity, and its success (number of claps) by employing natural language processing techniques. We highlight the emotions and polarity communicated by an article liable to increase the prediction regarding its acceptability by the audience.

Abstract. Gamification is a well known approach nowadays for achieving the ultimate goal of motivating loyalty with a brand and engaging users with given tasks in a variety of fields. However, attempts made to use gamification techniques in news industry have given poor results, pointing to the conclusion that journalism may not an open field for gamification to thrive. At the same time changes in news consumption across the globe show a rapid rise of news audiences accessing stories through their mobile devices, while social media play a huge role in distribution of online news. This paper examines the combination of these three trends, gamification, smartphones and social media in a promising and possibly successful way.

Uncovering their readers' perceptions is of key importance for every news media organization to find methods to improve the quality of their product. It has the potential to facilitate journalists' work in attracting attention and gaining a loyal audience. Discovering which elements of a news story influence readers' perceptions has been a cross-disciplinary research goal for the past years, because it can play a crucial role in news dissemination and consumption in the digital age. Drawing upon literature in the various areas such as journalism, psychology, computer science, and AI, this paper proposes a machine learning approach that explores three dimensions of article features that can help predicting the online behavior of the reader. Results show that how the story is written, the topic, and certain aspects of the author's online reputation can affect reader endorsements and the perceived quality of an article. CCS CONCEPTS • Computing methodologies → Natural language processing; • Applied computing → Document management and text processing.