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The article examines the issue of free speech in a law and economics perspective. The property rights approach is contrasted with the common law and constitutional standpoints. Consequentialist and market efficiency may not provide adequate criteria for judging limitations to freedom of speech. Constitutional instruments may then be required.

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.

This research is a critical discourse analysis (CDA) of Trump's speech on January 6, 2021, which results in his supporters' storming the US Capitol in order to challenge certifying Biden's victory. The Democrats accused Trump of incitement of insurrection. Consequently, Trump was impeached. This article investigates Trump's speech to label it as hate speech or free speech.

Analytical framework is tri-dimensional. The textual analysis is based on Halliday's notion of process types and Huckin's discourse tools of foregrounding and topicalization. The socio-cognitive analysis is based on Van Dijk's ideological square and his theory of mental models. The philosophical dimension is founded on Habermas's theory of discourse. These parameters are the cornerstones of the barometer that will be utilized to reach an objective evaluation of Trump's speech.

Findings suggest that Trump usually endows “I, We, You” with topic positions to lay importance on himself and his supporters. He frequently uses material process to urge the crowds' action. He categorizes Americans into two conflicting poles: He and his supporters versus the media and the Democrats. Mental models are created and activated so that the other is always negatively depicted. Reports about corruption are denied in court. Despite that, Trump repeats such reports. This is immoral in Habermas's terms. The study concludes that Trump delivered hate speech in order to incite the mob to act in a manner that may change the election results.

The study is original in its tri-dimensional framework and its data of analysis.

It is the contention of this paper that the state of siege on the part of students against academics and administrators of the 1960s has been replaced by one led by university administrators who are now waging a war of “political correctness” against students and faculty. These administrators were in large part the radicals of the 1960s. The present paper attempts to analyze the effects of government involvement in this industry on this phenomenon and the role of free speech rights in the presence and absence of marketplace considerations.

Discusses political correctness and the economics of higher education.

The contention is that competition brings about a better product at a lower price, and that the educational sector is no exception to this general rule. If free enterprise were allowed to operate in this context, much of these difficulties would disappear.

The paper offers insights into today's higher education industry and how economic analysis can explain the current “state of siege” on university campuses.

This study seeks to understand the opinions of internet users toward extreme speech on social media platforms and their willingness to censor such speech. The purpose of this paper is to examine how norms of freedom of expression are changing in an online communication environment dominated by these platforms.

Four focus groups were conducted in this study. Participants needed to use at least one social media platform daily. Groups were homogeneous in terms of race and gender: African-American females, African-American males, white females and white males.

Participants in general did not report a strong willingness to censor extreme speech on social media platforms. Rather, they expressed apathy and cynicism toward both their own and social media companies’ ability to combat extreme speech and make online discourse more positive. Female participants tended to value the overall health of public discourse and protection of more vulnerable social media users on social media platforms. African-American female participants called for platforms to recognize a special duty to protect minority users, whom they saw as responsible for the platforms’ success.

Focus groups are useful for providing exploratory rather than generalizable data. However, by increasing the understanding of how individuals define extreme speech on social media, these data can reveal how individuals rhetorically shape the social media platforms and interpret their role in democratic discourse.

This research takes the rich field of studying tolerance toward extreme speech to new territory: the online realm where public discourse (and especially extreme discourse) is hosted more and more.

The recent adoption by the US Federal Trade Commission of the national Do‐Not‐Call Registry prohibiting most telemarketers from contacting individuals once their number is added to the listing is a threat to the US domestic telemarketing industry. Analysis of US federal law suggests that the national registry is likely an unconstitutional exercise of administrative power. To be legal, governmental prior restraints on commercial speech must pass the four‐part Central Hudson test adopted by the US Supreme Court. The Do‐Not‐Call Registry fails parts three and four of the test and probably part two as well. There is an insufficient fit between the stated governmental purpose of the regulation and its operation. Moreover, balancing the benefits from the registry against the harm it inflicts further militates against its constitutionality. International and public relations implications are explored and discussed.

I begin with a discussion of code and its primary types: embedded, source, binary and interpreted. I then consider three measures in which code is fundamentally different than print. In particular I speak of the trust inherent in connectivity, the organizational difficulties of information, and the problem of archiving information that may change rapidly. Following each of these explanations I offer my own hypotheses about how code and ubiquitous digital media might alter society and the sensibilities of its participants.

The purpose of this study is to examine the effectiveness of learning Cantonese opera using Variation Theory as a guiding principle of pedagogical design.

A total of three types of speech in Cantonese opera were identified as the objects of learning in the study: patter speech, poetic speech and supported speech. Two classes of Grade five (N =54) students were arranged into an experimental group and a control group. In the experimental group, the characteristics of the three types of speech were taught using comparison and contrast techniques to help the learners identify and differentiate the characteristics of each type. The control group was taught the three types of speech sequentially without attempting to compare and contrast among the types. Pre‐ and post‐tests were conducted to test the students’ understanding of the objects of learning. An independent samples t‐test and a paired sample t‐test were applied to measure the statistical differences between the groups and the pre‐ and post‐tests, respectively.

The results indicated that there were no significant differences between the two groups before the treatment (p = .665). However, the experimental group achieved a significantly higher level of learning effectiveness than the control group after teaching (p = .003). The control group did not significantly improve their understanding after learning (p = .061), whereas the experimental group significantly increased their scores (p < .001).

The application of Variation Theory in academic subjects, especially mathematics, has been well explored in the literature. This study shows the application of Variation Theory in the cultural subject of music, and points to the potential of Variation Theory as a guiding principle of music teaching to enhance learning effectiveness.

The eternal question posed by students, “Why do I have to learn this?” is being answered for them every day in the newspapers and on television with respect to the balance of liberty and security in time of war. Teachers often express the need for focused materials that approach this question from both historical and modern perspectives, and this high-school lesson provides that. The Latin maxim, Inter arma enim silent leges, translated, “In time of war the laws are silent” expresses the doctrine that security trumps liberty in wartime, but in this lesson, student will ask, “Is liberty necessarily the price of security? How have United States governments justified the curtailment of liberty in wartime?” This lesson presents students and teachers with hands-on focus activities, student manipulatives and role-plays, and primary source document analyses that will lead students to appraise the cost of security and whether the Constitution can be preserved by being abridged.