Visualisation Methods for Diachronic Semantic Shift (2022)

Abstract: The meaning and usage of a concept or a word changes over time. These diachronic semantic shifts reflect the change of societal and cultural consensus as well as the evolution of science. The availability of large-scale corpora and recent success in language models have enabled researchers to analyse semantic shifts in great detail. However, current research lacks intuitive ways of presenting diachronic semantic shifts and making them comprehensive. In this paper, we study the PubMed dataset and compute semantic shifts across six decades. We develop three visualisation methods that can show, given a root word: the temporal change in its linguistic context, word re-occurrence, degree of similarity, time continuity, and separate trends per publisher location. We also propose a taxonomy that classifies visualisation methods for diachronic semantic shifts with respect to different purposes.

## introduction diachronic semantic shift or concept drift studies how a language (the meaning and usage of words) evolve over time @xcite . studying such semantic shifts is valuable for researchers who are interested in either the societal and cultural evolution, or the development of scientific research. in the latter case, innovations and groundbreaking discoveries often introduce new concepts, bring new meanings to existing ones, or shift existing meanings completely. automatically identifying and understanding diachronic semantic shifts is thus desirable. the availability of large-scale corpora @xcite and recent success in language models (tum, 2020) have enabled researchers to analyze semantic shifts in great detail @xcite @xcite . most of the research focuses on discovering general trends in semantic shifts, tracing the dynamics of the relationships between words, and elaborates on the methods used to detect such a shift @xcite . little research has explored the visual representation of such semantic shifts to help understand them intuitively. the visuals used across multiple studies include word graphs @xcite , scatterplots @xcite , and storylines @xcite . however, these methodologies currently fail in explicitly showing the temporal changes of a word and require the user to have some domain knowledge to fully comprehend the drifts. there is a strong need for further exploring the nature of this semantic shift by employing new visualization methods to make the semantic shift understandable, explainable, and explorable. the goal of the this study, therefore, is to present a classification of visualization methods for a word's semantic shift based on the type of concept the user wishes to analyze, which leads us to the following objectives: in this paper, we compute diachronic semantic shifts in pubmed across six decades, and propose three visualization methods utilising radial bars, spiral lines and word-cloud maps that can show, given a root word: the temporal change in its linguistic context, word re-occurrence, degree of similarity, time continuity, and separate trends per geographic location. we compare different visualization methods and propose a taxonomy that classifies methods for visualizing diachronic semantic shift. ## data our study is based on pubmed (national library of medicine, 2022), a large, long-term corpus of citations and abstracts of biomedical literature. we include articles from 1970 onward (when abstracts are available). we randomly sample 106 out of the available 1114 xml data files, to keep the data relatively balanced over the 52 years, enough to create a word embedding for each decade. we then divide the corpus into six decades, from the 1970s to the 2020s. ## results first, the jaccard index (the context similarity of any word between any two decades) shows a clear trend for each combination of decades, and various root words. namely, many root words have a jaccard similarity close to 0 (they exhibit a near-total change in their context across the time periods). however, these root words are not common scientific terms. the distribution of jaccard index values has a long tail towards the maximum value 1, and the more interesting terms (such as those presented in the next examples) lie on this tail. figure 1 shows the radial bar charts for the terms "anxiety", "cigarette", "coronavirus", and "misinformation". we see that the context around "anxiety" and "cigarette" stay relatively stable, but the difference is that the context of "anxiety" is much stronger than that of "cigarette" as the length of the bars does not change much among the top 50 contextual words. the lower left radial bar chart shows that the context of "coronavirus" (especially in the 2020s) has changed dramatically, suggesting how the research around coronavirus has shifted its focus due to the ongoing covid-19 pandemic. for the relatively modern word "misinformation", interesting patterns are visualized. from being nearly com-pletely missing in the 1970s, its context has largely changed, with quite different degrees of similarity. words such as "journalist" and "trump" show up in the 2010s, owing to the rise of the "fake news" phenomena that was prevalent at the time, and words like "twitter", "antivaccination", "netizens", "celebrity", "socialmedia", "facebook", and "instagram" in the 2020s, signifying social media and the internet as primary modes through which information, or in this case misinformation, is being disseminated. in figure 2 , we track the continuity of the word "anxiety" over time. we have manually annotated the category a word may belong to and assigned a colour to each of them. the continuity of the word can be seen if the line corresponding to the word is present in all the decades. some words, such as "alienation" and "apprehension" are only present in a single decade, while "fear" is present in the initial few decades, moves out of the word context, and is present in the later decades again. words such as "anger", "mood", and "selfesteem" are present across all decades and can be seen as uninterrupted lines across all the time periods. figure 3 shows the geography of the word "divorce" over three pairs of decades, respectively 1970s/1980s, 1900s/2000s and 2010s/2020s. the countries selected are usa, uk and the netherlands and the choice was motivated by the large volume of articles that they presented in the dataset. as can be observed from the picture, an interesting pattern is identified; during the 1970s and 1980s divorce had context words related to social status and level of education. however, in more recent years, the word seems to be more associated to the negative consequences that divorce itself can cause, such as increased criminal behaviour, violence and self-harm. this phenomenon is more apparent in the usa and the netherlands, but is not as evident in the uk, where divorce is still closely related to paternity and motherhood. ## user study to test the usability of these visualizations and measure whether they provide the desired benefit, we conducted a user study incorporating a selfassessment questionnaire. this study was conducted using the "visengage" questionnaire developed for interactive visualizations by @xcite . our questionnaire consisted of 11 questions which were grouped together based on type of characteristics they were meant to measure, which were, aesthetics, ease of finding information, usability, and user engagement. for each question, participants provide their response on a seven-point likert scale, ranging from strongly disagree (1) to strongly agree (7). our study consisted of 5 visualizations, including previous visualization techniques from related work, the proposed methods in this paper, and the same information in tabular data for a comparison of usability. for each visualization, the study consisted of 2 task-related questions whose answers could be found within the visualization, the questionnaire for measuring the categories, and an open feedback form for the user to share their opinion on the visualization. the results of this study from 8 participants have been summarized figure 4 . the heatmap shows that the average scores of the proposed radial bar chart and spiral line chart are 4.96 and 4.43 respectively, both above a "neutral" score (4), whereas, from the related work, only the word graph (4.7) scores above a "neutral" score, with the scatterplot scoring a 3.74 and plain tabular data scoring 3.77. the radial bar chart performs best in categories of aesthetics, usability, and user engagement, and is second to the word graph in ease of finding information. we see that the word graph performs better overall compared to the spiral line chart (except in the aesthetics category), and is a close second to the radial bar chart overall, allowing it to also be a viable option for visualizing semantic shifts. these scores from the study, taken in conjunction with the features visualized by each chart from ## conclusion in this paper we have studied the diachronic semantic shift of words over time and have proposed methods to visualize these shifts. we perform a user study to test the usability of the proposed methods. our interactive visualization tool helps users explore and understand these semantic shift. we also study previous visualization methods by other researchers and compare them to our proposed methods with a classification taxonomy. in the future, we will study the complete pub-meb dataset and apply different methods to identify semantic shifts. more metrics need to be developed to further quantify the semantic shift so that highly dynamic words can be identified automatically. furthermore, instead of manually annotating radial bar charts and selecting words for storylines, we will explore different possibilities of automatic annotation and selection. this method does not capture the context of multiple words occurring together. for example, the meaning of the words "vaccine" and "news" may stay the same throughout time, but the context in which these words occur together can differ across @xcite (1990s-2000s) (2010s-2020s) figure 3 : word-cloud maps (@xmath0, the netherlands, and usa) time periods (these 2 words could have different neighbours if considered during the coronavirus pandemic). we will also develop additional ways of visualizing diachronic semantic shift. figure 4 : heatmap of average scores for each question of each item across 5 visualizations. values 1 and 7 have been removed from the score legend as they were never used by the participants. sections have been superimposed for discussion purposes.