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Text segmentation is task often overlooked in NLP. The process of pre-digesting text effectively, such that NLP and machine-learning models can efficiently use it is a nuanced and delicate process. If segmented spans are too long, they may contained information that will hinder or hoodwink the model processing them, however, if the segments are too short one may lose the important context that glues those particular words together. Moreover, different models my require text to be segmented differently to other model to properly carry out their task. For example, if an analyst want to boil a large piece of text down to its constituent sentences, we will need our segmentations to reflect the sentences we indent to return. Alternatively, if we want to determine topical shifts in large textual inputs, our segments will need to be of paragraph-length. FuzzySeg is presented as a solution to the process of text segmentation. FuzzySeg aims to adopt fuzzy systems to improve upon rule-based and heuristic approaches to text segmentation. Segments of text are retrieved by means of boundary insertions carried our by the model. Inputs, derived from syntax parse trees generated from the text, determine the levels of cohesion at particular intervals. The fuzzy system aims to take these inputs to determine the probability of a boundary. Furthermore, the aim is to build on this method in future work with the goal of presenting a multifaceted segmentation approach that is applicable across various domains that require segmentation e.g. text summarisation and rhetorical structure theory. This is made possible due to the nature of fuzzy rule-generation capabilities that are created based on the syntactic data retrieved from inputted text. This data is subsequently used to train the model allowing us to produce the segmentation outputs. In this work, this work's function is to focus mainly on the extension of the work sur- rounding the applications of rhetorical structure theory for smartly weighting sentiment- carrying text. FuzzySeg aims to better segment the text within a key stage of rhetorical structure theory, with the aims of improving the accuracy of conventional sentiment anal- ysis methods. Summarised, briefly, this work contributes first and foremost to the field of text seg- mentation and fuzzy system through the introduction of fuzzy text segmentation - a novel union of both fields. Furthermore, this work focuses on the contribution to the province of rhetorical structure theory by means of introducing our presented fuzzy segmentation as the first stage of rhetorical structure theory parsing. Finally, the goal is apply the work in a novel practical capacity by means of using our fuzzy-segmentation-enhanced model for the use of sentiment analysis. The novel theoretical application for microaggression detection is also explore. The following thesis is presented as follows: the previously established concepts that were used to develop our understanding of segmentation and fuzzy systems are outlined first. Secondly, this work moves to outlining the motivations together with our overall position and reasons for the proposed method. The model, its components, and the validation metrics are then outlined, followed by descriptions on how the inputs and architecture for the fuzzy system are derived. The applications of the segmentation model together with the future work to be carried out on top of this model as well as alternative applications our model or similar models can be used for i.e. microaggression detection are discussed and concluded.

\"The Rise of Victimhood Culture offers a framework for understanding recent moral conflicts at U.S. universities, which have bled into society at large. These are not the familiar clashes between liberals and conservatives or the religious and the secular: instead, they are clashes between a new moral culture--victimhood culture--and a more traditional culture of dignity. Even as students increasingly demand trigger warnings and \"safe spaces,\" many young people are quick to police the words and deeds of others, who in turn claim that political correctness has run amok. Interestingly, members of both camps often consider themselves victims of the other. In tracking the rise of victimhood culture, Bradley Campbell and Jason Manning help to decode an often dizzying cultural milieu, from campus riots over conservative speakers and debates around free speech to the election of Donald Trump.\"--Back cover.

ABSTRACT While it is commonly thought that microaggressions are isolated incidents, microaggressions are ingrained throughout the academic research institution (Young, Anderson and Stewart 2015; Lee et al. 2020). Persons Excluded from science because of Ethnicity and Race (PEERs) frequently experience microaggressions from various academicians, including graduate students, postdocs and faculty (Asai 2020; Lee et al. 2020). Here, we elaborate on a rationale for concrete actions to cope with and diminish acts of microaggressions that may otherwise hinder the inclusion of PEERs. We encourage Science, Technology, Engineering and Mathematics (STEM) departments and leadership to affirm PEER scholar identities and promote allyship by infusing sensitivity, responsiveness and anti-bias awareness. This article focuses on how mentors can be allies against microaggressions in STEM.

IntroductionMicroaggressions, or subtle expressions of discrimination directed towards individuals because of their membership in marginalized social groups, are the subject of a growing body of literature (Sue, 2010). As a result of growing understanding of politically correct beliefs over time, they’ve been defined as subtler types of discrimination that have replaced formerly overt discrimination. Microaggressions differ from traditional prejudice in that they are frequently perpetrated by well-intentioned people who are oblivious of the negative implications and consequences of their conduct. Microaggressions have been documented in a variety of social groups, including racial/ethnic minorities (Sue et al., 2008; Torres et al., 2010), gender (Swim et al., 2001), sexual orientation (Shelton and Delgado-Romero, 2011), and ability status (Shelton and Delgado-Romero, 2011). Many people with mental illnesses have reported social rejection experiences that are similar to microaggressions, according to research (Cechnicki et al., 2011; Lundberg et al., 2009; Wright et al., 2000; Yanos et al., 2001).ObjectivesExisting measures of stigmatizing attitudes and behaviors may not capture much of the nuance in behavior that people with mental illness report to be particularly upsetting, so we thought it would be important to examine reliability and validity of the mental illness microaggressions scale-perpetrator version (MIMS-P) for measuring microaggression behavior in the general public in Turkey.MethodsThe methodological study will be conducted to establish the validity and reliability of the The mental illness microaggressions scale-perpetrator version (MIMS-P) scale to Turkish Culture and to determine the microaggression levels against individuals with mental illness in the general population. The sample of the study will consist of individuals who are reached through an online questionnaire and who agree to participate in the study. Individuals who have psychiatric disorders will not be included in the study.ResultsData collection process is still ongoing. Description of studies and the key findings will be presented.ConclusionsThe MIMS-P is designed to aid future study on the frequency of endorsement of microaggressions performed against people with mental illnesses, with the ultimate goal of understanding the mechanisms that lead to these acts.The development of an extra scale to measure microaggressions from the perspective of people with mental illnesses who encounter them is one of the future research objectives.With a better knowledge of these viewpoints and how they interact, effective therapies and public policy initiatives for reducing stigma against mental illness can be developed.Disclosure of InterestNone Declared

Abstract Age-related microaggressions are forms of ageist discrimination that occur during day-to-day interactions. The aim of this study was to identify common types of age-related microaggressions as well as to determine how negative affect influences emotional reactions to microaggressions. Using an online survey, participants (n = 200) were asked if they had experienced any of the 20 most common examples of age-related microaggressions reported in previous research (Gietzen et al, 2022). Follow up questions inquired about the frequency, emotional reactions, and behavioral responses to these microaggressions. Participants also rated their physical health and completed the Positive and Negative Affect Scale (PANAS; Watson et al., 1988). The results indicated that participants were familiar with these microaggressions 53% of the time. Participants also reported having negative reactions to 43% of these microaggressions. The frequency of negative emotional responses to microaggressions was significantly correlated with scores on the negative affect subscale of the PANAS (r = .34, p < .001) and with ratings of perceived physical health (r = -.32, p = .002). Finally, an analysis of the 20 survey items revealed that two items were “highly impactful” microaggressions, defined as microaggressions that older adults reported experiencing often and reported having a negative emotional reaction to at least one-third of the time. The results of the study provide further insight into what age-related microaggressions look like, and how older adults experience these interactions.