Quality Effects on User Preferences and Behaviorsin Mobile News Streaming

Effects of Incidental Brief Exposure to News on News Knowledge While Scrolling Through Videos

“…Although there may be the risk of incidental exposure for spreading misinformation due to exposure sensationalized titles and/or thumbnails without reading in social media [31], [32], [34], at least, our result did not indicate negative association between incidental brief exposure and news knowledge. This may be because ABEMA offers mass media contents which tend not to show biased titles.…”

“…Particularly, video hosting services and online video ads can increase this risk because they are available to everyone, including evil organizations and individuals, unlike in non-internet televisions and ABEMA. Actually, problematic/biased media using video hosting services and online video ads tends to use sensationalized and misleading titles and/or thumbnails for attracting attention [31], [32]. Incidental exposure to such media can badly affect people's knowledge and belief [35], [36].…”

“…On the other hand, incidental exposure without reading contents can facilitate that people believe and spread misinformation. Several online media tend to use titles and/or thumbnails that are designed to attract attention, typically sensationalized or misleading [31], [32]. Many people do not read news articles which are found at incidental exposure in social media, and they do not read most articles [33] and share these articles without reading [34].…”

Modeling micro-interactions in self-regulated learning: A data-driven methodology

“…We also considered the sessions where students were 'inactive' (i.e. f ij = 0), as dwell time is indirectly related with achievement through engagement [88,53]. Also, to show the evolution of interactive behaviours, we computed the frequency trend to indicate the general direction in which the number of occurrences is developing.…”

Prediction of News Popularity via Keywords Extraction and Trends Tracking

“…The main objective for researchers was to build a machine learning model based on the title of a news article that predicts whether users will click on this title or not. Lu et al [10] performed an analysis of people's behavior when reading online news articles. They found that users are more likely to click on low-quality articles because of their high title persuasion.…”