Abstract
WhatsApp is an instant messaging application for information exchange in real time. It is a medium for communication and interaction among individuals, groups, institutions and business partners. Enormous amount of information is generated by WhatsApp in velocity, volume and variety which can serves as a source for various analyses, prediction and for other purposes. In this paper, dataset was collected from WhatsApp Group Chat, FUDMA ASUU MATTERS (FAM), a chat group of lecturers from Academic Staff Union of University (ASUU), Federal University Dutsin-Ma, Katsina state Nigeria. The primary goal is to present detailed analysis of the WhatsApp group chat to ascertain the level of involvement and participation by members in the group. Detailed analysis of fact such as the number of messages sent in different format, the most active date and time as well as the most active user(s) is to be investigated. Text classification method with Python and Jupyter notebook was used. The Python libraries applied include, Numpy, Pandas, Matplotlib and Seaborn. The result has shown that the level of participation of members compared to top ten members is by far uneven as only the top ten members accounted for more than half of the cumulative messages sent over a period of fourteen months. The research encourages members to be actively involved instead of allowing few members to dominate the platform. It is better to be an active contributor rather than remaining as a passive onlooker
Highlights
Internet and its associated technologies have continued to revolutionize information exchanged
The goal of this paper is to present detailed analysis of the WhatsApp group chat to ascertain the level of involvement and participation by members in the group
Figure 4a and 4b show the distribution of the various messages in all posts of the group chat. 63.4% of all the chats amounting to 10,393 messages were Emojis
Summary
Internet and its associated technologies have continued to revolutionize information exchanged. Messages exchange by group members appears irrelevant as investigated by Ahmad et al (2021). Their findings revealed that of the total messages of over sixteen thousand, only 8.7% were found to be relevant messages, which is very insignificant compared to a significant percentage of messages found to be irrelevant constituting 43.3% of the total messages posted over a period of fourteen months. Dominate most social network group (Ahmad et al 2021). These and other factors compel groups to set out rules and regulations to guide exchange of information with limited or no compliance by recalcitrant members. Opinion mining is a type of natural language processing to track opinions of people about a particular event or subject (Harshal et al, 2018)
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