LAMBRETTA: Learning to Rank for Twitter Soft Moderation

To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing more context to their audiences. Unfortunately, these labels are not applied uniformly and leave large amounts of false content unmoderated. This paper presents LAMBRETTA, a system that automatically identifies tweets that are candidates for soft moderation using Learning To Rank (LTR). We run LAMBRETTA on Twitter data to moderate false claims related to the 2020 US Election and find that it flags over 20 times more tweets than Twitter, with only 3.93% false positives and 18.81% false negatives, outperforming alternative state-of-the-art methods based on keyword extraction and semantic search. Overall, LAMBRETTA assists human moderators in identifying and flagging false information on social media.Comment: 44th IEEE Symposium on Security & Privacy (S&P 2023

SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice

To counter online abuse and misinformation, social media platforms have been establishing content moderation guidelines and employing various moderation policies. The goal of this paper is to study these community guidelines and moderation practices, as well as the relevant research publications to identify the research gaps, differences in moderation techniques, and challenges that should be tackled by the social media platforms and the research community at large. In this regard, we study and analyze in the US jurisdiction the fourteen most popular social media content moderation guidelines and practices, and consolidate them. We then introduce three taxonomies drawn from this analysis as well as covering over one hundred interdisciplinary research papers about moderation strategies. We identified the differences between the content moderation employed in mainstream social media platforms compared to fringe platforms. We also highlight the implications of Section 230, the need for transparency and opacity in content moderation, why platforms should shift from a one-size-fits-all model to a more inclusive model, and lastly, we highlight why there is a need for a collaborative human-AI system

Estimation of ventricles size of human brain by Magnetic Resonance Imaging in Nepalese Population: A retrospective study

Background and Objective: Magnetic resonance imaging (MRI) provides image acquisition of three-dimensional data and measurement in any chosen imaging plane. Objective of this study is to assess the size of ventricles of the brain of normal Nepalese people and establish the range of size of the ventricular system and compute the ventricular dimensions among different age and gender. Materials and methods: This is a cross-sectional retrospective study done at Gandaki Medical College, Pokhara. A total of 106 MRI scan data of healthy individuals were collected over a period of seven months between March to September 2019. Patients ranged between eight and eighty years of age with 58 males and 48 females. Measurements of the mean of bifrontal diameter (BFD), bihemispheric diameter (BHD), third ventricle transverse dimension (TVTD), fourth ventricle antero-posterior dimension (FVAP), fourth ventricle width (FVW), and frontal horn ratio (FHR) were done. Result: The mean of BFD, BHD, TVTD, FVAP, FVW, and FHR were found to be 3.05 ± 0.10 cm, 10.11 ± 0.40 cm, 0.43 ± 0.11 cm, 0.90 ± 0.11 cm, 1.22 ± 0.12 cm, and 0.30 ± 0.01 cm, respectively. The mean width of fourth ventricle in males and females was observed to be 1.23 ± 0.12 cm and 1.19 ± 0.11 cm respectively. There was a significant correlation of TVTD, FVAP, FHR and BFD with age with Pearson correlation coefficient 0.393 (P value <0.01), 0.259 (P value <0.01), 0.34 (P value <0.01), and 0.219 (P value <0.05) respectively. However, BHD and FVW have no correlation with age. Conclusion: Third Ventricle Traverse Dimension, FVP, FVW and FHR show almost similar or slight difference in measurement according to gender. However, BFD shows larger difference in measurement according to gender. Similarly there is no such significant difference according to age in measurement of BFD, BHD, FVAP, FVW and FHR, while TVTD measurement shows slight increased measurement according to age

Lambretta: Learning to Rank for Twitter Soft Moderation

To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing more context to their audiences. Unfortunately, these labels are not applied uniformly and leave large amounts of false content unmoderated. This paper presents LAMBRETTA, a system that automatically identifies tweets that are candidates for soft moderation using Learning To Rank (LTR). We run Lambretta on Twitter data to moderate false claims related to the 2020 US Election and find that it flags over 20 times more tweets than Twitter, with only 3.93% false positives and 18.81% false negatives, outperforming alternative state-of-the-art methods based on keyword extraction and semantic search. Overall, LAMBRETTA assists human moderators in identifying and flagging false information on social media.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Organisation & Governanc