A crowd-sourcing approach for translations of minority language user-generated content (UGC)

Data sparsity is a common problem for machine translation of minority and less-resourced languages. While data collection for standard, grammatical text can be challenging enough, efforts for collection of parallel user-generated content can be even more challenging. In this paper we describe an approach to collecting English↔Irish translations of user-generated content (tweets) that overcomes some of these hurdles. We show how a crowd-sourced data collection campaign, which was tailored to our target audience (the Irish language community), proved successful in gathering data for a niche domain. We also discuss the reliability of crowd-sourcing English↔Irish tweet translations in terms of quality by reporting on a self-rating approach along with qualified reviewer ratings

Automatically applying a credibility appraisal tool to track vaccination-related communications shared on social media

Background: Tools used to appraise the credibility of health information are time-consuming to apply and require context-specific expertise, limiting their use for quickly identifying and mitigating the spread of misinformation as it emerges. Our aim was to estimate the proportion of vaccination-related posts on Twitter are likely to be misinformation, and how unevenly exposure to misinformation was distributed among Twitter users. Methods: Sampling from 144,878 vaccination-related web pages shared on Twitter between January 2017 and March 2018, we used a seven-point checklist adapted from two validated tools to appraise the credibility of a small subset of 474. These were used to train several classifiers (random forest, support vector machines, and a recurrent neural network with transfer learning), using the text from a web page to predict whether the information satisfies each of the seven criteria. Results: Applying the best performing classifier to the 144,878 web pages, we found that 14.4\% of relevant posts to text-based communications were linked to webpages of low credibility and made up 9.2\% of all potential vaccination-related exposures. However, the 100 most popular links to misinformation were potentially seen by between 2 million and 80 million Twitter users, and for a substantial sub-population of Twitter users engaging with vaccination-related information, links to misinformation appear to dominate the vaccination-related information to which they were exposed. Conclusions: We proposed a new method for automatically appraising the credibility of webpages based on a combination of validated checklist tools. The results suggest that an automatic credibility appraisal tool can be used to find populations at higher risk of exposure to misinformation or applied proactively to add friction to the sharing of low credibility vaccination information.Comment: 8 Pages, 5 Figure

Should I Trust Social Media? How Media Credibility and Language Affect False Memory

This study examined the influence of credibility and .language in Internet-based media on false memory. A randomized factorial 2 (media credibility) × 2 (language) experimental design was conducted with 106 college students. The two groups of media credibility consisted of social media (LINE) and non-social media (detik.com), while media language consisted of formal and informal language. A confidence test was used to measure false memory. A two-factor ANOVA showed that media credibility significantly affects false memory. Participants in the detik.com group were more confident in the information received and had greater false memory than the LINE group. However, no significant effect of language was found, and no significant interaction effect between media credibility and language on false memory was found. This study suggests that individuals should be cautious when reading information on non-social media platforms, as individuals tend to place more confidence on the source, leading to greater false memory

How many others have shared this? Experimentally investigating the effects of social cues on engagement, misinformation, and unpredictability on social media

Unlike traditional media, social media typically provides quantified metrics of how many users have engaged with each piece of content. Some have argued that the presence of these cues promotes the spread of misinformation. Here we investigate the causal effect of social cues on users' engagement with social media posts. We conducted an experiment with N=628 Americans on a custom-built newsfeed interface where we systematically varied the presence and strength of social cues. We find that when cues are shown, indicating that a larger number of others have engaged with a post, users were more likely to share and like that post. Furthermore, relative to a control without social cues, the presence of social cues increased the sharing of true relative to false news. The presence of social cues also makes it more difficult to precisely predict how popular any given post would be. Together, our results suggest that -- instead of distracting users or causing them to share low-quality news -- social cues may, in certain circumstances, actually boost truth discernment and reduce the sharing of misinformation. Our work suggests that social cues play important roles in shaping users' attention and engagement on social media, and platforms should understand the effects of different cues before making changes to what cues are displayed and how