Tweeting for change:how Twitter users practice hashtag activism through #BlackLivesMatter and #MeToo

Abstract. This paper examines the way protest movements operate online with the help of hashtags. By focusing on two social justice movements in particular, #BlackLivesMatter and #MeToo, the different purposes for employing these certain hashtags are explored in detail. This is done by collecting tweets posted on Twitter alongside the two hashtags and conducting a detailed analysis of the texts in order to assess the different purposes for which the hashtags are used. Since this study is focused on the online landscape, the theoretical background is rooted in digital interaction; the methodology employed in the actual data analysis is critical discourse analysis, and more specifically, digital discourse analysis. Although activism has shaped society for centuries, the emergence of the internet and social media has brought a new dimension to the way protest movements spread their message and gather new supporters. In order to give a sense of the historical weight behind both #BlackLivesMatter and #MeToo, work done by activists from earlier generations is discussed through examples from American society in the post-war era, specifically from the point of view of the civil rights movement and second-wave feminism. Some of the research questions this paper attempts to address are: for what purposes are the #BlackLivesMatter and #MeToo hashtags most often employed on Twitter? Do people tend to comment on the issues and ideologies represented by the hashtags, or are they more likely to employ the hashtags in completely unrelated tweets? What kind of linguistic or rhetorical practices are used in the tweets in connection with the hashtags? Through these research questions, this study examines the way Twitter users participate in the creation and progression of these two hashtag movements. The purpose of examining the topic of hashtag activism is to better understand how the use of social media affects the way protest movements work, and — in a wider scope — how online activism affects society itself. Findings made from the data suggest that the #BlackLivesMatter and #MeToo hashtags are mainly used for sharing messages that support the movements and their ideologies, while tweets that criticize the movements occur less often. The hashtags are often employed for framing news stories in from different perspectives to highlight injustices in society; this suggests that Twitter is used as a tool to rewrite or retell stories from media outlets in a way that highlights the unique experiences of certain societal groups. Another clear purpose for employing the hashtags is to use them in completely unrelated tweets, possibly as a tool to attract attention. This suggest that when a hashtag becomes widely known, its original purpose may become trivialized.Tiivistelmä. Tämä työ tutkii tapaa, millä protestiliikkeet toimivat netissä hashtagien avulla. Tutkimuksen kohteena ovat kaksi yhteiskunnallista protestiliikettä, #BlackLivesMatter ja #MeToo, sekä ne eri syyt, joiden vuoksi Twitterin käyttäjät hyödyntävät näitä hashtageja twiiteissaan. Tutkimus suoritetaan keräämällä twiitteja, joissa on käytetty edellä mainittuja hashtageja, ja suorittamalla yksityiskohtainen analyysi niiden kielestä; näin voidaan suorittaa päätelmiä niistä eri tarkoituksista, joiden vuoksi ihmiset jakavat twiitteja joissa esiintyy hashtag #BlackLivesMatter tai #MeToo. Koska tämä työ keskittyy nettiin ja sosiaaliseen mediaan, sen teoreettinen tausta kumpuaa digitaalisen vuorovaikutuksen tutkimuksista. Analyysin suoritusta varten käytössä on kriittinen diskurssianalyysi, ja myös digitaalinen diskurssianalyysi. Vaikka netissä toimivat protestiliikkeet ovat hyvin uusi ilmiö, kansalaisliikkeillä on hyvin pitkä historia. Koska tämä tutkimus koskee amerikkalaisia protestiliikkeitä, niiden taustaa käydään läpi historiallisten esimerkkien avulla: #BlackLivesMatter -liikkeen taustaa tutkitaan Yhdysvaltain kansalaisoikeusliikkeen kautta, kun taas #MeToo -liikettä tarkastellaan toisen aallon feminismin näkökulmasta. Tämä työ pyrkii vastaamaan muun muassa seuraaviin tutkimuskysymyksiin: mihin tarkoituksiin #BlackLivesMatter ja #MeToo hashtageja käytetään Twitterissä? Kommentoivatko ihmiset yleensä twiiteissaan protestiliikkeiden tavoitteita ja tarkoitusperiä, vai käytetäänkö hashtageja yleisemmin aivan toisenlaisiin tarkoituksiin? Millaisia kielellisiä tapoja twiiteista löytyy? Näiden kysymysten avulla tämä tutkimus tarkastelee tapaa, millä Twitterin käyttäjät osallistuvat yhteiskunnallisten protestiliikkeiden kehittymiseen. Laajemmalti tämä tutkimus pyrkii selvittämään sitä, miten sosiaalinen media vaikuttaa protestiliikkeiden toimintaan, ja sitä kautta myös koko yhteiskuntaan. Tutkimuksen löydöt osoittavat, että #BlackLivesMatter ja #MeToo hashtageja käytetään useimmiten liikkeiden aatteita kannattavien twiittien jakamiseen, kun taas liikkeitä kritisoivat viestit ovat harvinaisempia. Hashtageja käytetään usein muotoilemaan uutisia eri näkökulmista, jotka nostavat etualalle yhteiskunnallisia epäkohtia. Näin Twitterin käyttäjät pyrkivät tuomaan esiin sellaisten ihmisryhmien kokemuksia, joita ei usein käsitellä valtavirran mediassa. Tämän lisäksi hashtageja käytetään usein twiiteissa, jotka eivät kommentoi mitenkään #BlackLivesMatter tai #MeToo -aatteisiin liittyen; tästä voi päätellä, että tullessaan tunnetuksi suuren yleisön keskuudessa hashtagien edustamat aatteet voivat jäädä taka-alalle

Urban biodiversity and carbon sinks - Do they overlap? Case study of Helsinki Metropolitan Area, Finland.

Publisher Copyright: © Published under licence by IOP Publishing Ltd.Amongst the greatest global environmental challenges of our time are climate change and biodiversity loss. Feedback mechanisms associated with warming climate could also lead to large-scale biodiversity losses worldwide and it would therefore be logical to seek mitigation methods beneficial for both impact categories. However, research on the topic remains relatively scarce. Our study focuses on two key aspects of environmental sustainability, carbon storage capacity and species biodiversity, to determine whether these correlate at different levels of urban density. GIS-datasets are utilized to estimate the carbon storage potential and species diversity across the urban landscape as well as their association at different levels of urban land use intensity. The results highlight the importance of small green spaces at dense urban cores, indicating that in environments where green infrastructure is limited high species diversity and carbon storage are more likely to overlap, whereas at urban fringe the observed relationship is weaker and divergence of the two impact categories becomes more probable. The study draws attention to the role fragmented, limited green spaces play at establishing functioning ecosystems at local scale and provides new information to support the development of sustainable planning and management practices across the urban land use gradient.Peer reviewe

You’ll change more than I will:Adults’ predictions about their own and others’ future preferences

It has been argued that adults underestimate the extent to which their preferences will change over time. We sought to determine whether such mis-predictions are the result of a difficulty imagining that one’s own current and future preferences may differ or whether it also characterizes our predictions about the future preferences of others. We used a perspective- taking task in which we asked young people how much they liked stereotypically-young-person items (e.g., Top 40 music, adventure vacations) and stereotypically-old-person items (e.g., jazz, playing bridge) now, and how much they would like them in the distant future (i.e., when they are 70 years old). Participants also made these same predictions for a generic same-age, same-sex peer. In a third condition, participants predicted how much a generic older (i.e., age 70) same-sex adult would like items from both categories today. Participants predicted less change between their own current and future preferences than between the current and future preferences of a peer. However, participants estimated that, compared to a current older adult today, their peer would like stereotypically-young items more in the future and stereotypically-old items less. The fact that peers’ distant-future estimated preferences were different from the ones they made for “current” older adults suggests that even though underestimation of change of preferences over time is attenuated when thinking about others, a bias still exists

Improving neural network representations using human similarity judgments

Deep neural networks have reached human-level performance on many computer vision tasks. However, the objectives used to train these networks enforce only that similar images are embedded at similar locations in the representation space, and do not directly constrain the global structure of the resulting space. Here, we explore the impact of supervising this global structure by linearly aligning it with human similarity judgments. We find that a naive approach leads to large changes in local representational structure that harm downstream performance. Thus, we propose a novel method that aligns the global structure of representations while preserving their local structure. This global-local transform considerably improves accuracy across a variety of few-shot learning and anomaly detection tasks. Our results indicate that human visual representations are globally organized in a way that facilitates learning from few examples, and incorporating this global structure into neural network representations improves performance on downstream tasks.Comment: Published as a conference paper at NeurIPS 202

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Blood and cerebrospinal fluid neurofilament light differentially detect neurodegeneration in early Alzheimer's disease

Cerebrospinal fluid (CSF) neurofilament light (NfL) concentration has reproducibly been shown to reflect neurodegeneration in brain disorders, including Alzheimer’s disease (AD). NfL concentration in blood correlates with the corresponding CSF levels, but few studies have directly compared the reliability of these 2 markers in sporadic AD. Herein, we measured plasma and CSF concentrations of NfL in 478 cognitively unimpaired (CU) subjects, 227 patients with mild cognitive impairment, and 113 patients with AD dementia. We found that the concentration of NfL in CSF, but not in plasma, was increased in response to Ab pathology in CU subjects. Both CSF and plasma NfL concentrations were increased in patients with mild cognitive impairment and AD dementia. Furthermore, only NfL in CSF was associated with reduced white matter microstructure in CU subjects. Finally, in a transgenic mouse model of AD, CSF NfL increased before serum NfL in response to the development of Ab pathology. In conclusion, NfL in CSF may be a more reliable biomarker of neurodegeneration than NfL in blood in preclinical sporadic AD

SODA: Bottleneck Diffusion Models for Representation Learning

We introduce SODA, a self-supervised diffusion model, designed for representation learning. The model incorporates an image encoder, which distills a source view into a compact representation, that, in turn, guides the generation of related novel views. We show that by imposing a tight bottleneck between the encoder and a denoising decoder, and leveraging novel view synthesis as a self-supervised objective, we can turn diffusion models into strong representation learners, capable of capturing visual semantics in an unsupervised manner. To the best of our knowledge, SODA is the first diffusion model to succeed at ImageNet linear-probe classification, and, at the same time, it accomplishes reconstruction, editing and synthesis tasks across a wide range of datasets. Further investigation reveals the disentangled nature of its emergent latent space, that serves as an effective interface to control and manipulate the model's produced images. All in all, we aim to shed light on the exciting and promising potential of diffusion models, not only for image generation, but also for learning rich and robust representations