Controllable Neural Story Plot Generation via Reinforcement Learning

Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model towards a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques.Comment: Published in IJCAI 201

Antecedents of support for social media content moderation and platform regulation: the role of presumed effects on self and others

This study examines support for regulation of and by platforms and provides insights into public perceptions of platform governance. While much of the public discourse surrounding platforms evolves at a policy level between think tanks, journalists, academics and political actors, little attention is paid to how people think about regulation of and by platforms. Through a representative survey study of US internet users (N = 1,022), we explore antecedents of support for social media content moderation by platforms, as well as for regulation of social media platforms by the government. We connect these findings to presumed effects on self (PME1) and others (PME3), concepts that lie at the core of third-person effect (TPE) and influence of presumed influence (IPI) scholarship. We identify third-person perceptions for social media content: Perceived negative effects are stronger for others than for oneself. A first-person perception operates on the platform level: The beneficial effects of social media platforms are perceived to be stronger for the self than for society. At the behavioral level, we identify age, education, opposition to censorship, and perceived negative effects of social media content on others (PME3) as significant predictors of support for content moderation. Concerning support for regulation of platforms by the government, we find significant effects of opposition to censorship, perceived intentional censorship, frequency of social media use, and trust in platforms. We argue that stakeholders involved in platform governance must take more seriously the attitudes of their constituents

Event Representations for Automated Story Generation with Deep Neural Nets

Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora. To date, recurrent neural networks that learn language models at character, word, or sentence levels have had little success generating coherent stories. We explore the question of event representations that provide a mid-level of abstraction between words and sentences in order to retain the semantic information of the original data while minimizing event sparsity. We present a technique for preprocessing textual story data into event sequences. We then present a technique for automated story generation whereby we decompose the problem into the generation of successive events (event2event) and the generation of natural language sentences from events (event2sentence). We give empirical results comparing different event representations and their effects on event successor generation and the translation of events to natural language.Comment: Submitted to AAAI'1

Online Political Comments: Americans Talk About the Election Through a “Horse-Race” Lens

This study examined whether user-generated comments posted on news stories about the 2016 U.S. presidential campaign focused on candidates’ policies or on horse-race elements of the election, such as who is winning or losing. Using a quantitative content analysis (n = 1,881), we found that most comments had neither horse-race nor policy elements, but that horse-race elements were more frequent in comments than policy, mirroring what is found in news coverage. The public were more likely to “like” or “upvote” comments that contained either policy or horse-race elements, relative to other comments, although the relationship was slightly stronger for horse race

Trust and Credibility in Web-Based Health Information: A Review and Agenda for Future Research

Background: Online sources are becoming increasingly important in health information seeking, such that they may have a significant effect on health care decisions and outcomes. Hence, given the wide range of different sources of online health information from different organisations and individuals, it is important to understand how information seekers evaluate and select the sources that they use, and, more specifically how they assess their credibility and trustworthiness. Objectives: This article reviews empirical studies on trust and credibility in the use of online health information. The article seeks to present a profile of the research conducted on trust and credibility in online health information seeking, to identify the factors that impact judgements of trustworthiness and credibility, and to explore the role of demographic factors affecting trust formation. On this basis, it aims to identify the gaps in current knowledge and to propose an agenda for future research. Methods: A systematic literature review was conducted. Searches were conducted using a variety of combinations of the terms: online health information, trust, credibility, and their variants, in four multi-disciplinary and four health-oriented databases. Articles selected were published in English from 2000 onwards; this process generated 3827 unique records. After the application of exclusion criteria, this was reduced to a final dataset of 73 articles, which was analysed in full. Results: Interest in this topic has persisted over the last 15 years, with articles being published in medicine, social science and computer science, and focussing mostly on the USA and the UK. Documents in the dataset fell into three categories: those using trust or credibility as a dependent variable, those using trust or credibility as an independent variable, and studies of the demographic factors that influence the role of trust or credibility in online health information seeking. There is a consensus that in terms of website design, clear layout and design, interactive features and the authority of the owner have a positive effect on trust or credibility, whilst advertising has a negative effect. With regard to content features, authority of the author, ease of use and content have a positive effect on trust or credibility formation. Demographic factors influencing trust formation are age, gender and perceived health status. Conclusions: There is considerable scope for further research. This includes: increased clarity of the interaction between the variables associated with health information seeking; increased consistency on the measurement of trust and credibility; a greater focus on specific online health information sources; and, enhanced understanding of the impact of demographic variables on trust and credibility judgement

Metadata-enhanced contrastive learning from retinal optical coherence tomography images

Supervised deep learning algorithms hold great potential to automate screening, monitoring and grading of medical images. However, training performant models has typically required vast quantities of labelled data, which is scarcely available in the medical domain. Self-supervised contrastive frameworks relax this dependency by first learning from unlabelled images. In this work we show that pretraining with two contrastive methods, SimCLR and BYOL, improves the utility of deep learning with regard to the clinical assessment of age-related macular degeneration (AMD). In experiments using two large clinical datasets containing 170,427 optical coherence tomography (OCT) images of 7,912 patients, we evaluate benefits attributed to pretraining across seven downstream tasks ranging from AMD stage and type classification to prediction of functional endpoints to segmentation of retinal layers, finding performance significantly increased in six out of seven tasks with fewer labels. However, standard contrastive frameworks have two known weaknesses that are detrimental to pretraining in the medical domain. Several of the image transformations used to create positive contrastive pairs are not applicable to greyscale medical scans. Furthermore, medical images often depict the same anatomical region and disease severity, resulting in numerous misleading negative pairs. To address these issues we develop a novel metadata-enhanced approach that exploits the rich set of inherently available patient information. To this end we employ records for patient identity, eye position (i.e. left or right) and time series data to indicate the typically unknowable set of inter-image contrastive relationships. By leveraging this often neglected information our metadata-enhanced contrastive pretraining leads to further benefits and outperforms conventional contrastive methods in five out of seven downstream tasks

Activity of the efflux pump inhibitor SILA 421 against drug-resistant tuberculosis

Organosilicon compounds are efflux pump inhibitors with potency as an antituberculosis drug. Of the organisilicon compounds tested, SILA 421 has been shown to have a highest potency as an antituberculosis drug (1). It shares the common pathways for antimycobacterial killing with other efflux pump inhibitors: it revealed direct in vitro activity against M. tuberculosis (1), it has been shown to modify resistance by inhibiting mdr-1 efflux pumps and has shown to enhance killing of M. tuberculosis by macrophages (1)

Trim17, novel E3 ubiquitin-ligase, initiates neuronal apoptosis

Accumulating data indicate that the ubiquitin-proteasome system controls apoptosis by regulating the level and the function of key regulatory proteins. In this study, we identified Trim17, a member of the TRIM/RBCC protein family, as one of the critical E3 ubiquitin ligases involved in the control of neuronal apoptosis upstream of mitochondria. We show that expression of Trim17 is increased both at the mRNA and protein level in several in vitro models of transcription-dependent neuronal apoptosis. Expression of Trim17 is controlled by the PI3K/Akt/GSK3 pathway in cerebellar granule neurons (CGN). Moreover, the Trim17 protein is expressed in vivo, in apoptotic neurons that naturally die during post-natal cerebellar development. Overexpression of active Trim17 in primary CGN was sufficient to induce the intrinsic pathway of apoptosis in survival conditions. This pro-apoptotic effect was abolished in Bax(-/-) neurons and depended on the E3 activity of Trim17 conferred by its RING domain. Furthermore, knock-down of endogenous Trim17 and overexpression of dominant-negative mutants of Trim17 blocked trophic factor withdrawal-induced apoptosis both in CGN and in sympathetic neurons. Collectively, our data are the first to assign a cellular function to Trim17 by showing that its E3 activity is both necessary and sufficient for the initiation of neuronal apoptosis. Cell Death and Differentiation (2010) 17, 1928-1941; doi: 10.1038/cdd.2010.73; published online 18 June 201