From empathy to exclusion: how Immigrant groups are framed in online discourse

PurposeThe rise of social media and its resultant impact on brand management has become a critical factor in guarding the reputation of the firm. Consumer‐generated content has the potential to spread rapidly over social networks and the implications are that advertising as traditionally used by brand managers, now offers little control over the communication message. Brand managers need a better tool to gauge the changing mood in social media conversations. The purpose of this paper is to suggest a powerful method, Chernoff Faces, to compare six Sauternes wine brands based on social conversation measurement.Design/methodology/approachThis study describes a source of data relating to wine brand visibility in social media, and then presents a simple yet powerful graphical tool for portraying this information. This tool facilitates the communication, understanding, and assimilation of the relevant information.FindingsThe findings of this paper are presented in six social media wine faces. Facial features are allocated to eyes, facial line, hair density and others to reflect “Social Mention” data measuring brand strength, positive and negative sentiment and related elements such passion for the brand. A brief subjective interpretation of the differences between the wine brands offers a match between Chernoff faces representation and historical data on the brands being compared.Research limitations/implicationsThe paper has some limitations related to the dynamic nature of social media. This study provides more of a snapshot in time rather than an ultimate set of results. Future research could be done by closely monitoring the results for a set of brands over a period. A new option to overcome this by using longitudinal data is offered as a option in future research.Originality/valueSince social media are multi‐dimensional and attempts to understand conversations it requires tracking different measures simultaneously. It is important to find the best way to portray and communicate this data so that wine marketing decision makers can quickly and easily compare changes in brand images. Using faces to accomplish this is an easy and novel way compared to more demanding multidimensional scaling techniques.

In the U.S. political conservatives hold less favorable attitudes than liberals about immigration and immigrant groups. We hypothesized that one reason for this ideological gap is that conservatives are more likely to believe that immigrants are not as justifying of the American system as they should be. This hypothesis was tested in an online study (N = 404) with respect to four immigrant groups: Europeans, East Asians, Middle Easterners, and Latin Americans. Results revealed that conservatism was positively associated with (a) prescriptive beliefs that immigrants should engage in high levels of system justification, and (b) descriptive beliefs that immigrants—except for Middle Eastern immigrants—generally do endorse high levels of system justification. Importantly, conservatives perceived a bigger difference than liberals between prescriptive and descriptive beliefs about immigrants’ system justification levels, and this difference mediated the association between conservatism and attitudes and feelings about non-European (but not European) immigrants. These findings support a new “Perceived System Justification Deficit Model of Prejudice” in which expectations about others’ degree of ideological support for the societal status quo may contribute to out-group bias and perhaps even discrimination.

BackgroundA scoping review to characterize the literature on the use of conversations in social media as a potential source of data for detecting adverse events (AEs) related to health products.MethodsOur specific research questions were (1) What social media listening platforms exist to detect adverse events related to health products, and what are their capabilities and characteristics? (2) What is the validity and reliability of data from social media for detecting these adverse events? MEDLINE, EMBASE, Cochrane Library, and relevant websites were searched from inception to May 2016. Any type of document (e.g., manuscripts, reports) that described the use of social media data for detecting health product AEs was included. Two reviewers independently screened citations and full-texts, and one reviewer and one verifier performed data abstraction. Descriptive synthesis was conducted.ResultsAfter screening 3631 citations and 321 full-texts, 70 unique documents with 7 companion reports available from 2001 to 2016 were included. Forty-six documents (66%) described an automated or semi-automated information extraction system to detect health product AEs from social media conversations (in the developmental phase). Seven pre-existing information extraction systems to mine social media data were identified in eight documents. Nineteen documents compared AEs reported in social media data with validated data and found consistent AE discovery in all except two documents. None of the documents reported the validity and reliability of the overall system, but some reported on the performance of individual steps in processing the data. The validity and reliability results were found for the following steps in the data processing pipeline: data de-identification (n = 1), concept identification (n = 3), concept normalization (n = 2), and relation extraction (n = 8). The methods varied widely, and some approaches yielded better results than others.ConclusionsOur results suggest that the use of social media conversations for pharmacovigilance is in its infancy. Although social media data has the potential to supplement data from regulatory agency databases; is able to capture less frequently reported AEs; and can identify AEs earlier than official alerts or regulatory changes, the utility and validity of the data source remains under-studied.Trial registrationOpen Science Framework (https://osf.io/kv9hu/).

This chapter reviews major concepts, models, and theories to understand how to handle crises in social media and how social media affect crisis development and management. It discusses how social media influence stakeholder knowledge about crisis, as well as these stakeholders' understanding of crisis and their actions upon it. People use social media to seek updated information on critical situations, and to get emotional support. Social media conversations challenge organizational responses to critical issues because these conversations are not confined on specific network but can move from arena to arena and can even spread to offline arenas. Given that social media conversations can affect crisis awareness and interpretation among stakeholders, social media management has been recognized as an important element of crisis management, especially in supporting education of employees about social media. Limited knowledge exists about communicating with stakeholders in a more dialogical manner during crises, and even less research exists on dialogic communication via social media during crises.

e23008 Background: Social media has revolutionized scientific communication to extend engagement beyond peer-reviewed publications and traditional media channels. Hashtags (#) promote awareness of key topics and enable tracking of social media engagement (Katz et al. JAMA Oncol. 2016;2(3):392-4). We evaluated the utilization of Diversity, Equity, and Inclusion (#DEI) hashtags around large oncology meetings to describe the trends of DEI-focused social media conversations. Methods: Using Symplur Signals healthcare analytics on Twitter data, 5 dashboards with # datasets (#ASCO 18-22, #AACR 18-22, #ASH 18-22, #ASHDEI) were analyzed over 2018–2022. Data were then refined by content word filters (most used DEI hashtags: #cancerdisparities, #healthdisparities, #healthequity). Utilizing proprietary natural language processing combined with artificial intelligence, the Netbase Quid platform used a Boolean algorithm to collect ASCO DEI social media data (Twitter and others) to identify discussion themes between January 2021 – January 2023. Results: Use of the ASCO hashtag resulted in high and relatively stable social media activity from 2018-2022 (Table). In contrast, a major decline in both the DEI and DEI+ASCO hashtags occurred in 2022 after the peak in 2021 (Table). The peak use of DEI hashtags coincided with sentinel events in the US, including the deaths of George Floyd and Breonna Taylor and subsequent public protests. However, DEI conversations made up only 2% of ASCO’s social media conversations and accounted for far less for American Society of Hematology (ASH) and American Association for Cancer Research (AACR) (Table). Although smaller in number, AACR hashtags (#AACR, +DEI hashtags) were stable in 2021-22 compared with ASCO and ASH, coinciding with a dedicated conference focus on DEI topics during the AACR meeting. Conclusions: In general, oncology-associated Twitter activity appeared to be less frequent, possibly due to inconsistent use of DEI hashtags. To generate consistent and searchable DEI social media activity—and to communicate the outcomes of DEI initiatives—the oncology community should develop a lexicon of DEI hashtags. [Table: see text]

We extend our earlier work by focusing on broadcasting opinions (one-to-many interactions) alongside narrowcasts (one-to-one interactions) in social media conversations taking explicitly into consideration the behavioral characteristics of agents and the properties of the underlying network. In particular, we construct a generalized model for the spread of influence through broadcast and narrowcast interactions on social media discussion sites, and implement an agent-based model to develop insights regarding the effects of broadcasting. Our preliminary experiments show that increased broadcasting (in terms of frequency, depth, and number of broadcasters) increases homogeneity in an evolving scale-free network.

This article attempts to understand how ideas of justice are conceptualized within social media discourses around gender and sexual violence in India through an intersectional analysis of two case studies: the case of the rape and murder of a Hyderabad veterinarian and the List of Sexual Harassers in Academia, or LoSHA, which generated many conversations on sexual violence and justice on Twitter, Instagram, and Facebook. While a cursory reading of these two social media responses might see both as a rejection of due process and as seeking justice outside the structures of the judicial system, we argue that a closer reading of both is necessary to understand the different contours that these calls for justice have taken. In addition, we study how these two different social media conversations on sexual violence have centered the victim/survivor of sexual violence differently to understand how they engage with carcerality and anti-carceral politics. We conclude by attempting a definition of transformative justice based on anti-caste feminist interventions on social media, specifically drawing on the work of Dalit feminists, including those who created LoSHA.

Abstract: As sport organizations leverage social media as a critical component of marketing strategy, tools for exploring the large volume of sport consumer social media conversations are vital. This scholarship demonstrates the value of unsupervised latent Dirichlet allocation (LDA) as a tool for exploring consumers' digital conversations. Specifically, unsupervised LDA was applied to derive latent topics among Women's National Basketball Association-related Twitter conversation over the course of the 2020 season. Quantitative (cv and umass scores) and qualitative (two expert reviews) approaches were utilized to delineate topic configurations. Marginal topic distance established topic importance. Results from 118,518 tweets revealed 18 conversation topics spanning two overarching themes: social justice issues and on-court performance. The range and depth of the results highlight the importance of the unsupervised topic modeling method (without semi-supervised predetermined topic leads) for considering holistic rather than subsampled or snapshot datasets. This empirical investigation extends the conversation surrounding natural language processing to sport management research and practice, delivers a foundation for unsupervised LDA application to sport consumer conversation, and explores social media conversations during a critical moment for the WNBA.

IntroductionThis study aimed to characterize patient-reported outcomes from social media conversations in the gout community. The impact of management strategy differences on the community’s emotional states was explored.MethodsWe analyzed two social media sources using a variety of natural language processing techniques. We isolated conversations with a high probability of discussing disease management (score > 0.99). These conversations were stratified by management type: proactive or reactive. The polarity (positivity/negativity) of language and emotions conveyed in statements shared by community members was assessed by management type.ResultsAmong the statements related to management, reactive management (e.g., urgent care) was mentioned in 0.5% of statements, and proactive management (e.g., primary care) was mentioned in 0.6% of statements. Reactive management statements had a significantly larger proportion of negative words (59%) than did proactive management statements (44%); “fear” occurred more frequently with reactive statements, whereas “trust” predominated in proactive statements. Allopurinol was the most common medication in proactive management statements, whereas reactive management had significantly higher counts of prednisone/steroid mentions.ConclusionsA unique aspect of examining gout-related social media conversations is the ability to better understand the intersection of clinical management and emotional impacts in the gout community. The effect of social media statements was significantly stratified by management type for gout community members, where proactive management statements were characterized by more positive language than reactive management statements. These results suggest that proactive disease management may result in more positive mental and emotional experiences in patients with gout.

Social media is a collection of websites and applications mainly helpful for communication, interaction, sharing content and collaboration. The main purpose of this study is to find the human behavior at various times in the social media conversation. The people can be easily affected by toxic persons, messages or circumstances. Nowadays social media is used by most people for immediate reply. The textual data can easily make the people either cool or pulling down. The dealing of problems will be solved when one responds to the present rather than react. Unfortunately people are quickly reacting badly by conveying their messages in different forms either locally or internationally. This will lead to unnecessary emotions and opinions in a person’s mind. The Sentiment Analysis is the tool to observe and analyze human behavior by applying machine learning or deep learning algorithms. The aim of this review study is to enhance the process of finding human behavior by training computer machines using computer algorithms. Further it will give suggestions to change the mental attitude of social media users to deal with the present situation.

In the digital landscape, social media has emerged as a prevalent channel for global communication, connecting like-minded individuals worldwide. However, while facilitating information exchange, it is also susceptible to the dissemination of false information, posing a constant challenge to the reliability of online content. To address this issue, this paper introduces a novel methodology called TM-FID (Topic-oriented Multimodal False Information Detection), which combines false information detection and neural topic modeling within a semi-supervised multimodal approach. By jointly leveraging textual and visual information contained in online news, our approach provides insights into how false information influences specific discussion topics, thus enabling a comprehensive and fine-grained understanding of its spread and impact on social media conversation. Experimental evaluation carried out on a set of multimodal gossip-related news demonstrates the quality of the identified topics, assessed through a novel centroid-based metric, as well as the efficacy of the cross-attention mechanism used within TM-FID to accurately identify false information in multimodal news. Overall, the proposed methodology can enable effective strategies to counter the spread of false information, thereby fostering trust and confidence in the information shared on social media platforms.

The characterization and detection of bots with their presumed ability to manipulate society on social media platforms have been subject to many research endeavors over the last decade. In the absence of ground truth data (i.e., accounts that are labeled as bots by experts or self-declare their automated nature), researchers interested in the characterization and detection of bots may want to tap into the wisdom of the crowd. But how many people need to accuse another user as a bot before we can assume that the account is most likely automated? And more importantly, are bot accusations on social media at all a valid signal for the detection of bots? Our research presents the first large-scale study of bot accusations on Twitter and shows how the term bot became an instrument of dehumanization in social media conversations since it is predominantly used to deny the humanness of conversation partners. Consequently, bot accusations on social media should not be naively used as a signal to train or test bot detection models.

PurposeThis study aims to make two major contributions. First, given the literature gap in housing unaffordability for different immigrant groups in Canada, it makes an essential contribution to the literature. To the best of the knowledge, this study is the first study of its kind to examine housing unaffordability by examining different immigrant groups. Second, differences in unaffordability can help understand the decline in welfare, as it can have financial implications and a negative impact on health outcomes. Third, this study’s findings are valuable for policy formulation to improve immigrant integration and ease the housing unaffordability crisis.Design/methodology/approachThis study examines the determinants of housing affordability to investigate differences among various immigrant groups in Canada. A bivariate logit model using public microdata from the Canadian census estimates the determinants of moderate and severe unaffordability. Additionally, the separation of tenants and owners provides insights into the dynamics of unaffordability. The results show significant differences between immigrant groups with higher levels of unaffordability among Asian immigrants. The insights can help devise and implement housing assistance programs to address the challenges arising from the post-COVID-19 pandemic phase.FindingsThe results indicate that unaffordability declines with increasing age, education and full-time employment. Gender dynamics are evident, with women faring worse than men regarding the likelihood of extreme housing unaffordability. Households face a greater likelihood of unaffordability in more populous provinces and larger census metropolitan areas that struggle with the high cost of living, racial disparities and low income. Immigrants, especially from Asia, Africa and the Middle East, continue to struggle with chronic and severe unaffordability issues. The impact is much more severe for those renting, exemplifying the strain it is taking on the financial health of recent immigrants.Originality/valueGiven the literature gap in housing unaffordability for different immigrant groups in Canada, it makes an essential contribution to the literature. To the best of the knowledge, this study is the first study of its kind to examine housing unaffordability by examining different immigrant groups.

PurposeResearch on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media.Design/methodology/approachWe investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment.FindingsDifferent interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity.Originality/valueFindings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines.

The relative importance of marital and fertility characteristics upon female employment rates is tested in an attempt to explain the significant differential in women's nonagricultural participation rates between Latin American and Middle Eastern countries. Despite the striking differences in characteristics between the two populations with respect to variables centrally related to woman's employability, there is not enough evidence of the influence of these differences upon female employment rates. When the female population in Chile is subjected through standardization analysis to the same marital and fertility characteristics of Egypt, they continued to manifest high overall participation rates. Estimation of the womanpower potential in three Middle Eastern countries given their present marital characteristics shows that if women in these societies had the same propensity to be employed as women in Latin America, the overall nonagricultural female activity rate would increase threefold in Morocco, fivefold in Egypt and sevenfold in Pakistan. It is suggested that the explanation for the regional differential has to be sought in a comparative study of family and kinship organization between Latin American and Middle Eastern societies with special reference to the role of the kinship unit in the system of social control.