Purpose The purpose of this paper is to use the Twitter Search Network of the Apache NodeXL data discovery tool to extract over 5,000 data from Twitter accounts that twitted, re-twitted or commented on the hashtag, #NigeriaDecides, to gain insight into the impact of the social media on the politics and administration of developing countries. Design/methodology/approach Several algorithms like the Fruchterman-Reingold algorithm, Harel-Koren Fast Multiscale algorithm and the Clauset-Newman-Moore algorithms are used to analyse the social media metrics like betweenness, closeness centralities, etc., and visualize the sociograms. Findings Results from a typical application of this tool, on the Nigeria general election of 2015, show the social media as the major influencer and the contribution of the social media data analytics in predicting trends that may influence developing economies. Practical implications With this type of work, stakeholders can make informed decisions based on predictions that can yield high degree of accuracy as this case. It is also important to stress that this work can be reproduced for any other part of the world, as it is not limited to developing countries or Nigeria in particular or it is limited to the field of politics. Social implications Increasingly, during the 2015 general election, citizens have taken over the blogosphere by writing, commenting and reporting about different issues from politics, society, human rights, disasters, contestants, attacks and other community-related issues. One of such instances is the #NigeriaDecides network on Twitter. The effect of these showed in the opinion polls organized by the various interest groups and media houses which were all in favour of GMB. Originality/value The case study the authors took on the Nigeria’s general election of 2015 further strengthens the fact that the developing countries have joined the social media race. The major contributions of this work are that policy makers, politicians, business managers, etc. can use the methods shown in this work to harness and gain insights from Big Data, like the social media data.

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Social implications This study will help to promote a more positive society, ensuring the social media is positively utilized to the benefit of mankind. Originality/value 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 rapid spread of contagious diseases poses a colossal threat to human existence. Presently, the emergence of coronavirus COVID-19 which has rightly been declared a global pandemic resulting in so many deaths, confusion as well as huge economic losses is a challenge. It has been suggested by the World Health Organization (WHO) in conjunction with different Government authorities of the world and non-governmental organizations, that efforts to curtail the COVID-19 pandemic should rely principally on measures such as social distancing, identification of infected persons, tracing of possible contacts as well as effective isolation of such person(s) for subsequent medical treatment. The aim of this study is to provide a framework for monitoring Movements of Pandemic Disease Patients and predicting their next geographical locations given the recent trend of infected COVID-19 patients absconding from isolation centres as evidenced in the Nigerian case. The methodology for this study, proposes a system architecture incorporating GPS (Global Positioning System) and Assisted-GPS technologies for monitoring the geographical movements of COVID-19 patients and recording of their movement Trajectory Datasets on the assumption that they are assigned with GPS-enabled devices such as smartphones. Accordingly, fifteen (15) participants (patients) were selected for this study based on the criteria of residency and business activity location. The ensuing participants movements generated 157, 218 Trajectory datasets during a period of 3 weeks. With this dataset, mining of the movement trace, Stay Points (hot spots), relationships, and the prediction of the next probable geographical location of a COVID-19 patient was realized by the application of Artificial Intelligence (AI) and Data Mining techniques such as supervised Machine Learning (ML) algorithms (i.e., Multiple Linear Regression (MLR), k-Nearest Neighbor (kNN), Decision Tree Regression (DTR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and eXtreme Gradient Boosting regression(XGBR) as well as density-based clustering methods (i.e., DBSCAN) for the computation of Stay Points (hot spots) of COVID-19 patient. The result of this study showed clearly that it is possible to determine the Stay Points (hot spots) of a COVID-19 patient. In addition, this study demonstrated the possibility of predicting the next probable geographical location of a COVID-19 patient. Correspondingly, Six Machine Learning models (i.e., MLR, kNN, DTR, RFR, GBR, and XGBR) were compared for efficiency, in determining the next probable location of a COVID-19 patient. The result showed that the DTR model performed better compared to other models (i.e., MLR, kNN, RFR, GBR, XGBR) based on four evaluation matrices (i.e., ACCURACY, MAE, MSE, and R 2 ) used. It is recommended that less developed Countries consider adopting this framework as a policy initiative for implementation at this burgeoning phase of COVID-19 infection and beyond. The same applies to the...

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Customer relationship management (CRM) is a methodology and tool that possesses the plan and techniques that companies should follow in relating with their customers. In today’s business world, the relationship between organizations and their customers is very important in engaging the customers’ interest, which has the direct effect in increasing the business profit. Due to ineffective collaboration and interaction between organizations and their customers, identifying who the real customers are, along with what they need has failed. A breach of trust from the company, and lack of feedback from the customer could make an organization not to be able to compete with her rivals in a business environment and win her customers’ loyalty. Therefore, the guarantee of the customer continuing transactions with the company may no longer be assured. This work deploys an association rule learning technique of data mining using frequent pattern growth algorithm to identify which items are regularly purchased together by customers and based on this result, analyzes and understands the customers’ buying habits. Object-Oriented Analysis and Design methodology (OOAD) is used to analyze and design the system, whereas the implementation is carried out using Python programming language and My-SQL database management system. The contribution of this work is that it enables firms to gather and analyze customers’ interests in a product so that the needs of their valued customers will be met in order to make them return for more business transactions, thereby achieving customer retention.