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While the Afrocentric Worldview is established with elements of interdependence, communalism, and kinship at its foundation, many Afro (of African-descent) and African-American scholars within social science/helping-fields, such as psychology, have come to view “alternative” sexual orientations (i.e., homosexuality or bisexuality) as functional or dysfunctional solutions to problems existing in Black America. Afrocentric Worldviews include key concepts of racial and cultural survival thrusts. We must examine the marginalized subgroup of Black Lesbian, Gay, Bisexual, Trans-, and Queer (LGBTQ) individuals navigating through higher education, especially those within the Afrocentric-driven fields, such as psychology, at a Historically Black Colleges/Universities (HBCUs). This chapter discusses (1) several theoretical concepts that guide driving philosophies and academic curricula, (2) possible ramifications and experiences Black LGBTQ scholars face as they navigate through such educational contexts and (3) possible stances gay and straight scholars may take when operating under a paradigm/worldview with views that may seem counter to “alternative” sexual orientations.

In this chapter, the editors provide a reflective anecdote describing the professional and personal journey which led to the production of the current volume. The chapter presents the aim and scope of the text, chapter descriptions, and the overall goal of the text which includes facilitating conversations around how historically Black colleges or universities (HBCUs) might best support underserved populations of students and faculty.

Twitter is a popular microblogging service which has proven, in recent years, its potential for propagating news and information about developing events. The purpose of this paper is to focus on the analysis of information credibility on Twitter. The purpose of our research is to establish if an automatic discovery process of relevant and credible news events can be achieved.

The paper follows a supervised learning approach for the task of automatic classification of credible news events. A first classifier decides if an information cascade corresponds to a newsworthy event. Then a second classifier decides if this cascade can be considered credible or not. The paper undertakes this effort training over a significant amount of labeled data, obtained using crowdsourcing tools. The paper validates these classifiers under two settings: the first, a sample of automatically detected Twitter “trends” in English, and second, the paper tests how well this model transfers to Twitter topics in Spanish, automatically detected during a natural disaster.

There are measurable differences in the way microblog messages propagate. The paper shows that these differences are related to the newsworthiness and credibility of the information conveyed, and describes features that are effective for classifying information automatically as credible or not credible.

The paper first tests the approach under normal conditions, and then the paper extends the findings to a disaster management situation, where many news and rumors arise. Additionally, by analyzing the transfer of our classifiers across languages, the paper is able to look more deeply into which topic-features are more relevant for credibility assessment. To the best of our knowledge, this is the first paper that studies the power of prediction of social media for information credibility, considering model transfer into time-sensitive and language-sensitive contexts.