Mining HPV Vaccine Knowledge Structures of Young Adults From Reddit Using Distributional Semantics and Pathfinder Networks

The human papillomavirus (HPV) vaccine protects adolescents and young adults from 9 high-risk HPV virus types that cause 90% of cervical and anal cancers and 70% of oropharyngeal cancers. This study extends our previous research analyzing online content concerning the HPV vaccination in social media platforms used by young adults, in which we used Pathfinder network scaling and methods of distributional semantics to characterize differences in knowledge organization reflected in consumer- and expert-generated online content. The current study extends this approach to evaluate HPV vaccine perceptions among young adults who populate Reddit, a major social media platform. We derived Pathfinder networks from estimates of semantic relatedness obtained by learning word embeddings from Reddit posts and compared these to networks derived from human expert estimation of the relationship between key concepts. Results revealed that users of Reddit, predominantly comprising young adults in the vaccine catch up age-group 18 through 26 years of age, perceived the HPV vaccine domain from a virus-framed perspective that could impact their lifestyle choices and that their awareness of the HPV vaccine for cancer prevention is also lacking. Further differences in knowledge structures were elucidated, with implications for future health communication initiatives.

Using Pathfinder networks to discover alignment between expert and consumer conceptual knowledge from online vaccine content

This study demonstrates the use of distributed vector representations and Pathfinder Network Scaling (PFNETS) to represent online vaccine content created by health experts and by laypeople. By analyzing a target audience's conceptualization of a topic, domain experts can develop targeted interventions to improve the basic health knowledge of consumers. The underlying assumption is that the content created by different groups reflects the mental organization of their knowledge. Applying automated text analysis to this content may elucidate differences between the knowledge structures of laypeople (heath consumers) and professionals (health experts). This paper utilizes vaccine information generated by laypeople and health experts to investigate the utility of this approach. We used an established technique from cognitive psychology, Pathfinder Network Scaling to infer the structure of the associational networks between concepts learned from online content using methods of distributional semantics. In doing so, we extend the original application of PFNETS to infer knowledge structures from individual participants, to infer the prevailing knowledge structures within communities of content authors. The resulting graphs reveal opportunities for public health and vaccination education experts to improve communication and intervention efforts directed towards health consumers. Our efforts demonstrate the feasibility of using an automated procedure to examine the manifestation of conceptual models within large bodies of free text, revealing evidence of conflicting understanding of vaccine concepts among health consumers as compared with health experts. Additionally, this study provides insight into the differences between consumer and expert abstraction of domain knowledge, revealing vaccine-related knowledge gaps that suggest opportunities to improve provider-patient communication.

Reflective Random Indexing and indirect inference: a scalable method for discovery of implicit connections

The discovery of implicit connections between terms that do not occur together in any scientific document underlies the model of literature-based knowledge discovery first proposed by Swanson. Corpus-derived statistical models of semantic distance such as Latent Semantic Analysis (LSA) have been evaluated previously as methods for the discovery of such implicit connections. However, LSA in particular is dependent on a computationally demanding method of dimension reduction as a means to obtain meaningful indirect inference, limiting its ability to scale to large text corpora. In this paper, we evaluate the ability of Random Indexing (RI), a scalable distributional model of word associations, to draw meaningful implicit relationships between terms in general and biomedical language. Proponents of this method have achieved comparable performance to LSA on several cognitive tasks while using a simpler and less computationally demanding method of dimension reduction than LSA employs. In this paper, we demonstrate that the original implementation of RI is ineffective at inferring meaningful indirect connections, and evaluate Reflective Random Indexing (RRI), an iterative variant of the method that is better able to perform indirect inference. RRI is shown to lead to more clearly related indirect connections and to outperform existing RI implementations in the prediction of future direct co-occurrence in the MEDLINE corpus.

Visual misperception in aviation: glide path performance in a black hole environment

OBJECTIVE

We sought to improve understanding of visual perception in aviation to mitigate mishaps in approaches to landing.

BACKGROUND

Research has attempted to identify the most salient visual cues for glide path performance in impoverished visual conditions. Numerous aviation accidents caused by glide path overestimation (GPO) have occurred when a low glide path was induced by a black hole illusion (BHI) in featureless terrain during night approaches.

METHOD

Twenty pilots flew simulated approaches under various visual cues of random terrain objects and approach lighting system (ALS) configurations. Performance was assessed relative to the desired 3 degrees glide path in terms of precision, bias, and stability.

RESULTS

With the high-ratio (long, narrow) runway, the overall performance between 8.3 and 0.9 km from the runway depicted a concave approach shape found in BHI mishaps. The addition of random terrain objects failed to improve glide path performance, and an ALS commonly used at airports induced GPO and the resulting low glide path. The worst performance, however, resulted from a combination ALS consisting of both side and approach lights. Surprisingly, novice pilots flew more stable approaches than did experienced pilots.

CONCLUSIONS

Low, unsafe approaches occur frequently in conditions with limited global and local visual cues. Approach lights lateral of the runway may counter the bias of the BHI. The variability suggested a proactive, cue-seeking behavior among experienced pilots as compared with novice pilots.

APPLICATION

Visual spatial disorientation training in flight simulators should be used to demonstrate visual misperceptions in black hole environments and reduce pilots' confidence in their limited visual capabilities.