Detecting cyberstalking from social media platform(s) using data mining analytics

Cybercrime is an increasing activity that leads to cyberstalking whilst making the use of data mining algorithms to detect or prevent cyberstalking from social media platforms imperative for this study. The aim of this study was to determine the prevalence of cyberstalking on the social media platforms using Twitter. To achieve the objective, machine learning models that perform data mining alongside the security metrics were used to detect cyberstalking from social media platforms. The derived security metrics were used to flag up any suspicious cyberstalking content. Two datasets of detailed tweets were analysed using NVivo and R Programming. The dominant occurrence of cyberstalking was assessed with the induction of fifteen unigrams identified from the preliminary dataset such as “abuse”, “annoying”, “creep or creepy”, “fear”, “follow or followers”, “gender”, “harassment”, “messaging”, “relationships p/p”, “scared”, “stalker”, “technology”, “unwanted”, “victim”, and “violent”. Ordinal regression was used to analyse the use of the fifteen unigrams which were categorised according to degree or relationship/link towards cyberstalking on the platform Twitter. Moreover, two lightweight machine learning algorithms were used for the model performance showcasing cyberstalking indicative content. K Nearest Neighbour and K Means Clustering were both coded in R computer language for the extraction, refined, analysation and visualisation process for this research. Results showed the emotional terms like “bad”, “sad” and “hate” were attached to the unigrams being linked to cyberstalking. Each emotional term was flagged up in correspondence with one of the fifteen unigrams in tweets that correlate cyberstalking indicative content, proving one must accompany the other. K Means Clustering results showed the two terms “bad” and “sad” were shown within 100 percent of the clustering results and the term “hate” was only seen within 60 percent of the results. Results also revealed that the accuracy of the KNN algorithm was up to 40% in predicting key terms-based cyberstalking content in a real Twitter dataset consisting of 1m data points. This study emphasises the continuous relationship between the fifteen unigrams, emotional terms, and tweets within numerous datasets portrayed in this research, and reveals a general picture that cyberstalking indicative content in fact happens on Twitter at a vast rate with the corresponding links or relationships within the detection of cyberstalking

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

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Leading Towards Voice and Innovation: The Role of Psychological Contract

Background: Empirical evidence generally suggests that psychological contract breach (PCB) leads to negative outcomes. However, some literature argues that, occasionally, PCB leads to positive outcomes. Aim: To empirically determine when these positive outcomes occur, focusing on the role of psychological contract (PC) and leadership style (LS), and outcomes such as employ voice (EV) and innovative work behaviour (IWB). Method: A cross-sectional survey design was adopted, using reputable questionnaires on PC, PCB, EV, IWB, and leadership styles. Correlation analyses were used to test direct links within the model, while regression analyses were used to test for the moderation effects. Results: Data with acceptable psychometric properties were collected from 11 organisations (N=620). The results revealed that PCB does not lead to substantial changes in IWB. PCB correlated positively with prohibitive EV, but did not influence promotive EV, which was a significant driver of IWB. Leadership styles were weak predictors of EV and IWB, and LS only partially moderated the PCB-EV relationship. Conclusion: PCB did not lead to positive outcomes. Neither did LS influencing the relationships between PCB and EV or IWB. Further, LS only partially influenced the relationships between variables, and not in a manner which positively influence IWB

Assuming Data Integrity and Empirical Evidence to The Contrary

Background: Not all respondents to surveys apply their minds or understand the posed questions, and as such provide answers which lack coherence, and this threatens the integrity of the research. Casual inspection and limited research of the 10-item Big Five Inventory (BFI-10), included in the dataset of the World Values Survey (WVS), suggested that random responses may be common. Objective: To specify the percentage of cases in the BRI-10 which include incoherent or contradictory responses and to test the extent to which the removal of these cases will improve the quality of the dataset. Method: The WVS data on the BFI-10, measuring the Big Five Personality (B5P), in South Africa (N=3 531), was used. Incoherent or contradictory responses were removed. Then the cases from the cleaned-up dataset were analysed for their theoretical validity. Results: Only 1 612 (45.7%) cases were identified as not including incoherent or contradictory responses. The cleaned-up data did not mirror the B5P- structure, as was envisaged. The test for common method bias was negative. Conclusion: In most cases the responses were incoherent. Cleaning up the data did not improve the psychometric properties of the BFI-10. This raises concerns about the quality of the WVS data, the BFI-10, and the universality of B5P-theory. Given these results, it would be unwise to use the BFI-10 in South Africa. Researchers are alerted to do a proper assessment of the psychometric properties of instruments before they use it, particularly in a cross-cultural setting