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The purpose of this paper is to test the idea that symbols can serve as a cue to group membership and to assess discrimination towards working with individuals displaying certain symbols – the ichthus, the gay pride symbol and the Confederate flag.

This study looked at one particular method (i.e. clothing worn) of revealing one's attitude towards an issue or group, such as the Confederacy or Christianity. This study was designed to test selection preferences for three different symbols each against a control group. The experimental independent variable of symbol had four levels (control, ichthus, gay pride triangle, and Confederate flag). Two subject variables were tested as moderating variables (ethnic identity and Christian identity). Each of these was measured via a questionnaire, and a median split on scores was used to create two groups: strong and weak identity for each scale. The dependent variable was the selection preference for the target individual. Participants were 265 undergraduate students enrolled in introductory psychology and management classes.

It was confirmed that there are many signs that people give off in their verbal and non‐verbal behavior that reveal bits and pieces of their personality and ideologies.

The discrimination that students showed in this study reveals the importance of training those who may go into management roles and be involved in selection decisions to be aware of their natural tendencies to categorize people and the behavioral outcomes this can have.

Hate speech in recent times has become a troubling development. It has different meanings to different people in different cultures. The anonymity and ubiquity of the social media provides a breeding ground for hate speech and makes combating it seems like a lost battle. However, what may constitute a hate speech in a cultural or religious neutral society may not be perceived as such in a polarized multi-cultural and multi-religious society like Nigeria. Defining hate speech, therefore, may be contextual. Hate speech in Nigeria may be perceived along ethnic, religious and political boundaries. The purpose of this paper is to check for the presence of hate speech in social media platforms like Twitter, and to what degree is hate speech permissible, if available? It also intends to find out what monitoring mechanisms the social media platforms like Facebook and Twitter have put in place to combat hate speech. Lexalytics is a term coined by the authors from the words lexical analytics for the purpose of opinion mining unstructured texts like tweets.

This research developed a Python software called polarized opinions sentiment analyzer (POSA), adopting an ego social network analytics technique in which an individual’s behavior is mined and described. POSA uses a customized Python N-Gram dictionary of local context-based terms that may be considered as hate terms. It then applied the Twitter API to stream tweets from popular and trending Nigerian Twitter handles in politics, ethnicity, religion, social activism, racism, etc., and filtered the tweets against the custom dictionary using unsupervised classification of the texts as either positive or negative sentiments. The outcome is visualized using tables, pie charts and word clouds. A similar implementation was also carried out using R-Studio codes and both results are compared and a t-test was applied to determine if there was a significant difference in the results. The research methodology can be classified as both qualitative and quantitative. Qualitative in terms of data classification, and quantitative in terms of being able to identify the results as either negative or positive from the computation of text to vector.

The findings from two sets of experiments on POSA and R are as follows: in the first experiment, the POSA software found that the Twitter handles analyzed contained between 33 and 55 percent hate contents, while the R results show hate contents ranging from 38 to 62 percent. Performing a t-test on both positive and negative scores for both POSA and R-studio, results reveal p-values of 0.389 and 0.289, respectively, on an α value of 0.05, implying that there is no significant difference in the results from POSA and R. During the second experiment performed on 11 local handles with 1,207 tweets, the authors deduce as follows: that the percentage of hate contents classified by POSA is 40 percent, while the percentage of hate contents classified by R is 51 percent. That the accuracy of hate speech classification predicted by POSA is 87 percent, while free speech is 86 percent. And the accuracy of hate speech classification predicted by R is 65 percent, while free speech is 74 percent. This study reveals that neither Twitter nor Facebook has an automated monitoring system for hate speech, and no benchmark is set to decide the level of hate contents allowed in a text. The monitoring is rather done by humans whose assessment is usually subjective and sometimes inconsistent.

This study establishes the fact that hate speech is on the increase on social media. It also shows that hate mongers can actually be pinned down, with the contents of their messages. The POSA system can be used as a plug-in by Twitter to detect and stop hate speech on its platform. The study was limited to public Twitter handles only. N-grams are effective features for word-sense disambiguation, but when using N-grams, the feature vector could take on enormous proportions and in turn increasing sparsity of the feature vectors.

The findings of this study show that if urgent measures are not taken to combat hate speech there could be dare consequences, especially in highly polarized societies that are always heated up along religious and ethnic sentiments. On daily basis tempers are flaring in the social media over comments made by participants. This study has also demonstrated that it is possible to implement a technology that can track and terminate hate speech in a micro-blog like Twitter. This can also be extended to other social media platforms.

This study will help to promote a more positive society, ensuring the social media is positively utilized to the benefit of mankind.

The findings can be used by social media companies to monitor user behaviors, and pin hate crimes to specific persons. Governments and law enforcement bodies can also use the POSA application to track down hate peddlers.

The following classified, annotated list of titles is intended to provide reference librarians with a current checklist of new reference books, and is designed to supplement the RSR review column, “Recent Reference Books,” by Frances Neel Cheney. “Reference Books in Print” includes all additional books received prior to the inclusion deadline established for this issue. Appearance in this column does not preclude a later review in RSR. Publishers are urged to send a copy of all new reference books directly to RSR as soon as published, for immediate listing in “Reference Books in Print.” Reference books with imprints older than two years will not be included (with the exception of current reprints or older books newly acquired for distribution by another publisher). The column shall also occasionally include library science or other library related publications of other than a reference character.