Search results

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.

Sexual violence (SV) is a pervasive public health issue on college campuses. While much research has been conducted to determine factors contributing to SV, little work focuses on the role of perceived social norms. The purpose of this paper is to examine college students’ perceived descriptive norms for SV perpetration (i.e. prevalence estimates for SV).

Using a cross-sectional survey, male and female college students from a large public institution in the Southeastern USA were instructed to estimate the prevalence of SV for “typical students” of their same gender on campus.

When compared to actual prevalence rates of SV perpetrated by females and males, both perpetrators and non-perpetrators overestimated the prevalence of SV among same-sex peers, but perpetrators made even higher estimates compared to those made by non-perpetrators for some sexually aggressive acts. Results demonstrate strong and consistent normative misperceptions surrounding SV perpetration.

Findings lend support for testing social norms-based prevention programs for SV on college campuses.

This study is one of the first investigations into perceived social norms surrounding SV; perceived social norms may be an influential factor contributing to SV.

The genesis of “shareholder activism” in the USA may be traced back to several decades, but it only evolved in India at the start of the 21st century. This paper aims to explore the concept of “shareholder activism” in the Indian context, in light of the New Companies Act, 2013. The act is envisioned as a precursor to invoking the intention of shareholders to confront managers. Further, it aims to look at the possibilities of using tools of shareholder activism to make companies aware of their concerns.

Authors explore the concept of shareholder activism with the help of textual analysis, using R. Then, the authors study the mediating effects of “shareholder’s intention towards activism” between the “regulatory mechanisms” and “the usage of various tools of activism”, using the partial least square approach.

Regulatory mechanisms, such as the Companies Act, 2013, enhance the shareholders’ power to sensitise companies towards various corporate governance issues. It also increases their intention towards shareholder activism, eventually leading to favourable opinion on using various tools of “activism” in their investee companies.

This study is a unique attempt to assess the minority shareholders’ potential to become active in their investee companies induced by changes in the rules and regulations of a country.

Shareholder activism in India has not been thoroughly explored thus far. This paper specifically studies the opinions of retail investors, who possibly could increase companies’ accountability towards their minority shareholders, especially in light of the New Companies Act, 2013.