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This study aims to evaluate the fears of individuals on their profiles’ sharing in social network sites (SNSs), regarding its advantages and disadvantages. The researched issues are related with the employment seeking process. The concern of this study is the deviation observation between the fears acquired by the business and private social media members.

This study included an online survey with 236 respondents and calculated indicators of central tendency or location parameter, correlation coefficients and performed analysis of variance.

The result indicated and revealed the hidden danger and opportunities among social network members’ profile. This result addressed the need to consider the issue of user’s fears in reengineering the practical use of SNSs by organisations.

Interesting for further research would be to transfer this research from the employment seeking process in other research fields to generalise the results more accurately.

The contribution to the research field is to compare different SNSs and to explain the reasons to use SNS profiles to support organisations by their decisions for a valuable strategy. This study provides an insight in use and behaviour of SNS members that support researchers to understand the behaviour of SNS members regarding their profile under consideration of the employment seeking process.

Sarcasm is often used in everyday speech and writing and is prevalent in online contexts. The purpose of this paper is to investigate the analogy between sarcasm comments from sentiment tools and the human coder.

Using the Verbal Irony Procedure, eight human coders were engaged to analyse comments collected from an online commercial page, and a dissimilarity analysis was conducted with sentiment tools. Three constants were tested, namely, polarity from sentiment tools, polarity rating by human coders; and sarcasm-level ratings by human coders.

Results found an inconsistent ratio between these three constants. Sentiment tools used did not have the capability or reliability to detect the subtle, contextualized meanings of sarcasm statements that human coders could detect. Further research is required to refine the sentiment tools to enhance their sensitivity and capability.

With these findings, it is recommended that further research and commercialization efforts be directed at improving current sentiment tools – for example, to incorporate sophisticated human sarcasm texts in their analytical systems. Sarcasm exists frequently in media, politics and human forms of communications in society. Therefore, more highly sophisticated sentiment tools with the abilities to detect human sarcasm would be vital in research and industry.

The findings suggest that presently, of the sentiment tools investigated, most are still unable to pick up subtle contexts within the text which can reverse or change the message that the writer intends to send to his/her receiver. Hence, the use of the relevant hashtags (e.g. #sarcasm; #irony) are of fundamental importance in detection tools. This would aid the evaluation of product reviews online for commercial usage.

The value of this study lies in its original, empirical findings on the inconsistencies between sentiment tools and human coders in sarcasm detection. The current study proves these inconsistencies are detected between human and sentiment tools in social media texts and points to the inadequacies of current sentiment tools. With these findings, it is recommended that further research and commercialization efforts be directed at improving current sentiment tools – to incorporate sophisticated human sarcasm texts in their analytical systems. The system can then be used as a reference for psychologists, media analysts, researchers and speech writers to detect cues in the inconsistencies in behaviour and language.