From work emerging through the middle of the 20th century, the essence of meaning has become widely accepted as being described by the three orthogonal dimensions of valence, arousal, and dominance. These essential dimensions have become the cornerstone of sentiment analysis across many fields. By reexamining first types and then tokens for the English language, and through the use of automatically annotated histograms-"ousiograms"-we find here that the essence of meaning conveyed by words is instead best described by a goodness-power-aggression-danger-structure (GPADS) circumplex framework; that large-scale English language corpora reveal a systematic bias toward safe, low-danger words; and that the power-danger-structure framework is the minimal framework that represents essential meaning. We find remarkable congruences between the GPADS framework and other spaces including mental states and fictional archetypes, and we construct and demonstrate a prototype ousiometer.

Although nearly 600,000 people experience homelessness in the United States (U.S) every year, efforts to address this public health crisis are limited by the underperformance of standard methods to estimate localized and nationwide homelessness. Recent studies suggest social media activity can function as a proxy for measures of state-level public health, detectable through straightforward applications of natural language processing. We present the results of our efforts to apply this approach to estimate homelessness at the state level throughout the US during the period 2010-2019 and 2022 using a dataset of roughly 1 million geotagged tweets containing the substring “homeless.” Correlations between homelessness-related tweet counts and ranked per capita homelessness volume, but not general-population densities, suggest a relationship between the likelihood of Twitter users to personally encounter or observe homelessness in their everyday lives and their likelihood to communicate about it online. An increase in the log-odds of the word “homeless” appearing in an English-language tweet, as well as an acceleration in the increase in average tweet sentiment, suggest that tweets about homelessness are also affected by trends at the nation-scale. Additionally, changes to the lexical content of tweets over time suggest that reversals to the polarity of national or state-level trends may be detectable through an increase in political or service-sector language over the semantics of charity or direct appeals. While a computational approach to social media analysis may provide a low-cost, real-time dataset rich with information about nationwide and localized impacts of homelessness and homelessness policy, we find that practical issues abound, limiting the potential of social media as a proxy to complement other measures of homelessness.

Objective: Panic attacks are an impairing mental health problem that affects 11% of adults every year. Current criteria describe them as occurring without warning, despite evidence suggesting individuals can often identify attack triggers. We aimed to prospectively explore qualitative and quantitative factors associated with the onset of panic attacks. Results: Of 87 participants, 95% retrospectively identified a trigger for their panic attacks. Worse individually reported mood and state-level mood, as indicated by Twitter ratings, were related to greater likelihood of next-day panic attack. In a subsample of participants who uploaded their wearable sensor data (n=32), louder ambient noise and higher resting heart rate were related to greater likelihood of next-day panic attack. Conclusions: These promising results suggest that individuals who experience panic attacks may be able to anticipate their next attack which could be used to inform future prevention and intervention efforts.

Alkaline hydrolysis can lay claim to being a resource-efficient, effective, economical and environmentally sound method of final body disposition, relative to burial and cremation. On technical grounds it may have much to recommend it, however, like many other technical innovations, its take-up is hindered by the fact that it lacks a clear position in the public imagination. For this position to take shape, an understanding of just what it is and what it offers is required by proponents in the funeral industry who advise the bereaved, as well as by the material representations of the alkaline hydrolysis technologies themselves. In this article, we describe and analyse four extant alternative material and discursive forms of alkaline hydrolysis and how they variously occupy the fraught space where morality, death and marketing converge. Currently, each of the four forms of alkaline hydrolysis struggle to represent themselves in a public narrative that conveys their different ontologies and their competitive advantage, relative to burial and cremation, and this paper describes some key rhetorical and technical aspects of these struggles.

This chapter articulates the metaphorical entailments of two emerging strands of Western vernacular spirituality, the dead who become ‘trees’ and the dead who become ‘mushrooms’. Trees feature heavily across the spectrum of natural deathcare services, as symbols of generational legacy, or as a promise of a green afterlife. More recently, fungi have been incorporated into products designed to enhance natural burial, including burial suits, coffins and pods. We critically examine the emergence of arboreal and mycelium afterlives drawing on interviews conducted between 2018 and 2021 with death technologists in Australia, the United Kingdom and the United States, as well as analysis of their promotional materials and pitches at industry conferences, and analysis of the popular reception on news and social media. Both trees and mushrooms act as powerful ecological symbols, but offer up distinct configurations of the material and symbolic boundaries between human/nonhuman, corpse/nature, self/other. How is the human personhood or personal identity of the dead preserved, transformed or severed in the form of trees or fungi? What is the relationship between these trees or fungi and the community of the living? And what kind of afterlives are generated by the transformation of human life and decay into these life forms?

Human bodies are typically buried underground, horizontally 'in repose'. To the extent that this orientation has become the standard; it is a non-choice that is under-interrogated by scholars. In this paper, we discuss innovations which allow for the vertical orientation of the body within the earth and for the vertical stacking of remains above the earth in high-rise structures. Both of these boundary-pushing forms of disposition address imminent shortages in the land allocated for cemeteries in the context of intense urbanisation and a peaking death rate. They also promise to transform the necrogeography of contemporary cities and intimate relations between the living and the dead. This paper is a collaboration between the DeathTech Research Team and the Managing Director of Upright Burials, where the dead are 'stood to rest' in shaft graves. The pragmatic advantages of vertical burial are easily explicated, but in this paper, we focus on the cultural and symbolic dimensions of this largely unfamiliar spatial relation and the challenges of 'reorienting' the public towards this new form of disposition.

Digital technologies play an increasingly prominent role in memorialisation, but their use at cemeteries has been criticised for lack of sensitivity. Designers of digital cemetery technologies (cemtech) face a high risk of causing offence or failing to attract users due to social norms of memorial sites. To map this area of design and its pitfalls, we first developed a typology of cemtech through a review of examples from around the world. We then evaluated social acceptance of various types of cemtech through a survey of 1,053 Australian residents. Younger people were more accepting of cemtech than older people. Acceptance was highest for cemtech with three characteristics: familiarity, intimacy of user group and peacefulness. Through a reflexive thematic analysis, we identified four attitudinal dichotomies that explain divergent reactions to cemtech: Expands/Impedes, Public/Private, Lively/Restful and Pragmatic/Affective. We conclude with a discussion of how this work can assist designers of public memorialisation technologies.

Working at the intersection of death studies and media studies, this article examines what we can learn from the death of media technologies designed for the deceased, what we refer to as necro-technologies. Media deaths illuminate a tension between the promise of persistence and realities of precariousness embodied in all media. This tension is, however, more visibly strained by the mortality of technologies designed to mediate and memorialise the human dead by making explicit the limitations of digital eternity implied by products in the funeral industry. In this article, we historicise and define necro-technologies within broader discussions of media obsolescence and death. Drawing from our funeral industry fieldwork, we then provide four examples of recently deceased necro-technologies that are presented in the form of eulogies. These eulogies offer a stylised but culturally significant format of remembrance to create an historical record of the deceased and their life. These necro-technologies are the funeral attendance robot CARL, the in-coffin sound system CataCombo, the posthumous messaging service DeadSocial and the digital avatar service Virtual Eternity. We consider what is at stake when technologies designed to enliven the human deceased – often in perpetuity – are themselves subject to mortality. We suggest a number of entangled economic, cultural and technical reasons for the failure of necro-technologies within the specific contexts of the death care industry, which may also help to highlight broader forces of mortality affecting all media technologies. These are described as misplaced commercial imaginaries, cultural reticence and material impermanence. In thinking about the deaths of necro-technologies, and their causes, we propose a new form of death, a ‘material death’ that extends beyond biological, social and memorial forms of human death already established to account for the finitude of media materiality and memory.

Background Panic attacks are an impairing mental health problem that affects about one in 10 US adults every year. Current DSM criteria describe panic attacks as unexpected, occurring without warning or triggering events. The unexpected nature of panic attacks not only leads to increased anxiety for the individual but has also made panic attacks particularly challenging to study. However, recent evidence suggests that individuals who experience such attacks could identify attack triggers. Objective We aimed to explore both retrospectively and prospectively, qualitative, and quantitative factors associated with the onset of panic attacks. Method We remotely recruited a diverse sample of 87 individuals who regularly experienced panic attacks from 30 states in the US. Participants responded to daily questions relating to their panic attacks and wellness behaviors each day for 28 days. We also considered daily community level factors captured by the Hedonometer, a metric which estimates population-level happiness daily using a random 10% of all public tweets. Results Consistent with our prior work, most participants (95%) were able to retrospectively identify a trigger for their attack. Worse individual mood was associated with greater likelihood of experiencing a same-day panic attack over and above other individual wellness factors. Worse individually reported mood and state-based population level mood as indicated by the Hedonometer were associated with greater likelihood of next-day panic attack. Conclusions These promising results suggest that individuals who experience panic attacks may be able to expect the unexpected. The importance of individual and state-based population level mood in panic attack risk could be used to ultimately inform future prevention and intervention efforts.

Recent studies suggest social media activity can function as a proxy for measures of state-level public health, detectable through natural language processing. We present results of our efforts to apply this approach to estimate homelessness at the state level throughout the US during the period 2010-2019 and 2022 using a dataset of roughly 1 million geotagged tweets containing the substring ``homeless.'' Correlations between homelessness-related tweet counts and ranked per capita homelessness volume, but not general-population densities, suggest a relationship between the likelihood of Twitter users to personally encounter or observe homelessness in their everyday lives and their likelihood to communicate about it online. An increase to the log-odds of ``homeless'' appearing in an English-language tweet, as well as an acceleration in the increase in average tweet sentiment, suggest that tweets about homelessness are also affected by trends at the nation-scale. Additionally, changes to the lexical content of tweets over time suggest that reversals to the polarity of national or state-level trends may be detectable through an increase in political or service-sector language over the semantics of charity or direct appeals. An analysis of user account type also revealed changes to Twitter-use patterns by accounts authored by individuals versus entities that may provide an additional signal to confirm changes to homelessness density in a given jurisdiction. While a computational approach to social media analysis may provide a low-cost, real-time dataset rich with information about nationwide and localized impacts of homelessness and homelessness policy, we find that practical issues abound, limiting the potential of social media as a proxy to complement other measures of homelessness.

Digital technologies are creating new ways for visitors to engage with cemeteries. This article presents research into the development of digital cemetery technologies, or cemtech, to understand how they are reimagining memorial spaces. Through a systematic review of examples of cemtech in online records, academic literature, patents, and trade publications, we developed a typology of cemtech according to four characteristics: application type, technical components, target users, and development status. Analysis of the application types resulted in five higher-level themes of functionality or operation-Wayfinding, Narrativizing, Presencing, Emplacing, and Repurposing-which we discuss. This typology and thematic analysis help to identify and understand the development of cemetery technology design trajectories and how they reimagine possibilities for cemetery use and experience.

Well curated, large-scale corpora of social media posts containing broad public opinion offer an alternative data source to complement traditional surveys. While surveys are effective at collecting representative samples and are capable of achieving high accuracy, they can be both expensive to run and lag public opinion by days or weeks. Both of these drawbacks could be overcome with a real-time, high volume data stream and fast analysis pipeline. A central challenge in orchestrating such a data pipeline is devising an effective method for rapidly selecting the best corpus of relevant documents for analysis. Querying with keywords alone often includes irrelevant documents that are not easily disambiguated with bag-of-words natural language processing methods. Here, we explore methods of corpus curation to filter irrelevant tweets using pre-trained transformer-based models, fine-tuned for our binary classification task on hand-labeled tweets. We are able to achieve F1 scores of up to 0.95. The low cost and high performance of fine-tuning such a model suggests that our approach could be of broad benefit as a pre-processing step for social media datasets with uncertain corpus boundaries.

Online misogyny has become a fixture in female politicians' lives. Backlash theory suggests that it may represent a threat response prompted by female politicians' counterstereotypical, power-seeking behaviors. We investigated this hypothesis by analyzing Twitter references to Hillary Clinton before, during, and after her presidential campaign. We collected a corpus of over 9 million tweets from 2014 to 2018 that referred to Hillary Clinton, and employed an interrupted time series analysis on the relative frequency of misogynistic language within the corpus. Prior to 2015, the level of misogyny associated with Clinton decreased over time, but this trend reversed when she announced her presidential campaign. During the campaign, misogyny steadily increased and only plateaued after the election, when the threat of her electoral success had subsided. These findings are consistent with the notion that online misogyny towards female political nominees is a form of backlash prompted by their ambition for power in the political arena.

Complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Here, we introduce ‘allotaxonometry’ along with ‘rank-turbulence divergence’ (RTD), a tunable instrument for comparing any two ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence, which we view as an instrument of ‘type calculus’, for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry.

The murder of George Floyd by police in May 2020 sparked international protests and brought unparalleled levels of attention to the Black Lives Matter movement. As we show, his death set record levels of activity and amplification on Twitter, prompted the saddest day in the platform's history, and caused his name to appear among the ten most frequently used phrases in a day, where he is the only individual to have ever received that level of attention who was not known to the public earlier that same week. Importantly, we find that the Black Lives Matter movement's rhetorical strategy to connect and repeat the names of past Black victims of police violence-foregrounding racial injustice as an ongoing pattern rather than a singular event-was exceptionally effective following George Floyd's death: attention given to him extended to over 185 prior Black victims, more than other past moments in the movement's history. We contextualize this rising tide of attention among 12 years of racial justice activism on Twitter, demonstrating how activists and allies have used attention and amplification as a recurring tactic to lift and memorialize the names of Black victims of police violence. Our results show how the Black Lives Matter movement uses social media to center past instances of police violence at an unprecedented scale and speed, while still advancing the racial justice movement's longstanding goal to "say their names."

Sentiment-aware intelligent systems are essential to a wide array of applications. These systems are driven by language models which broadly fall into two paradigms: Lexicon-based and contextual. Although recent contextual models are increasingly dominant, we still see demand for lexicon-based models because of their interpretability and ease of use. For example, lexicon-based models allow researchers to readily determine which words and phrases contribute most to a change in measured sentiment. A challenge for any lexicon-based approach is that the lexicon needs to be routinely expanded with new words and expressions. Here, we propose two models for automatic lexicon expansion. Our first model establishes a baseline employing a simple and shallow neural network initialized with pre-trained word embeddings using a non-contextual approach. Our second model improves upon our baseline, featuring a deep Transformer-based network that brings to bear word definitions to estimate their lexical polarity. Our evaluation shows that both models are able to score new words with a similar accuracy to reviewers from Amazon Mechanical Turk, but at a fraction of the cost.

The Janus face metaphor approach highlights that a technology may simultaneously have two opposite faces or properties with unforeseen paradoxes within human-technology interaction. Suboptimal acceptance and clinical outcomes are sometimes seen in adolescents who use diabetes-related technologies. A traditional linear techno-determinist model of technology use would ascribe these unintended outcomes to suboptimal technology, suboptimal patient behavior, or suboptimal outcome measures. This paradigm has demonstratively not been successful at universally improving clinical outcomes over the last two decades. Alternatively, the Janus face metaphor moves away from a linear techno-determinist model and focuses on the dynamic interaction of the human condition and technology. Specifically, it can be used to understand variance in adoption or successful use of diabetes-related technology and to retrospectively understand suboptimal outcomes. The Janus face metaphor also allows for a prospective exploration of potential impacts of diabetes-related technology by patients, families, and their doctors so as to anticipate and minimize potential subsequent tensions.

Cybernetics has an exciting history that profoundly influenced HCI theory and practice. However, today, designers, engineers, and researchers are often unaware of how to engage with this powerful approach to inform technology design. We believe a cybernetic lens is vital to investigating and designing technologies for today's complex environments. In this in person, half-day workshop called "Cybernetic Lenses for Designing and Living in a Complex World", we present three cybernetic lenses demonstrated in two case studies: "From mundane objects to systems - asking better questions for technology design", and "Transportation networks, from human-computer interaction to socio-cultural consequences". Our goal is to offer an inclusive environment to provide theory and practical exercises for participants to gain and incorporate a cybernetics lens into their practice.

BACKGROUND:Mental health challenges are thought to affect approximately 10% of the global population each year, with many of those affected going untreated because of the stigma and limited access to services. As social media lowers the barrier for joining difficult conversations and finding supportive groups, Twitter is an open source of language data describing the changing experience of a stigmatized group. OBJECTIVE:By measuring changes in the conversation around mental health on Twitter, we aim to quantify the hypothesized increase in discussions and awareness of the topic as well as the corresponding reduction in stigma around mental health. METHODS:We explored trends in words and phrases related to mental health through a collection of 1-, 2-, and 3-grams parsed from a data stream of approximately 10% of all English tweets from 2010 to 2021. We examined temporal dynamics of mental health language and measured levels of positivity of the messages. Finally, we used the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language that was due to social amplification. RESULTS:We found that the popularity of the phrase mental health increased by nearly two orders of magnitude between 2012 and 2018. We observed that mentions of mental health spiked annually and reliably because of mental health awareness campaigns as well as unpredictably in response to mass shootings, celebrities dying by suicide, and popular fictional television stories portraying suicide. We found that the level of positivity of messages containing mental health, while stable through the growth period, has declined recently. Finally, we observed that since 2015, mentions of mental health have become increasingly due to retweets, suggesting that the stigma associated with the discussion of mental health on Twitter has diminished with time. CONCLUSIONS:These results provide useful texture regarding the growing conversation around mental health on Twitter and suggest that more awareness and acceptance has been brought to the topic compared with past years.

We explore the relationship between context and happiness scores in political tweets using word co-occurrence networks, where nodes in the network are the words, and the weight of an edge is the number of tweets in the corpus for which the two connected words co-occur. In particular, we consider tweets with hashtags #imwithher and #crookedhillary, both relating to Hillary Clinton’s presidential bid in 2016. We then analyze the network properties in conjunction with the word scores by comparing with null models to separate the effects of the network structure and the score distribution. Neutral words are found to be dominant and most words, regardless of polarity, tend to co-occur with neutral words. We do not observe any score homophily among positive and negative words. However, when we perform network backboning, community detection results in word groupings with meaningful narratives, and the happiness scores of the words in each group correspond to its respective theme. Thus, although we observe no clear relationship between happiness scores and co-occurrence at the node or edge level, a community-centric approach can isolate themes of competing sentiments in a corpus.