## 3 Materials and Methods
### Automated Content Analysis in Identifying Agriculture Social Network Themes and Concepts  
Content analysis (CA) examines the contents of a particular topic in the literature to identify feature patterns and themes [85]. Content analysis aims to show the information output with the circumstance in which they are generated [86] and varies between summative semantics and uncovering the content of illustrative communication [87]. Hence, content analysis can be both qualitative (targeted on understanding and interpreting) and quantitative (targeted on measuring and counting) [1; 88]. Different software tools are applied in content analysis, particularly social network analysis [89; 90]. Automated content analysis implements algorithms that use probabilistic models (such as terminological and concept mapping models) to reveal the dominant themes from the literature, their frequency, and their relationships [91]. The content analysis technique mechanism occurs in three phases: concept determination, concept definition, and text categorization [92; 93]. In the initial phase, concept determination, individual words frequently occurring in the content are noted as concept seeds [92]. In the next phase, concept definition, the automatic content analysis software tool generates the class of words that form a concept using a concept mapping algorithm or topic model. This formation phase results in a set of dominant concepts, each formed by their vocabulary [94; 95]. During the final phase, text categorization, the literature is categorized by the concepts located and defined based on the previous two phases. In text categorization, a text segment is analyzed for evidence of the occurrence of each concept [94]. After the execution of the three phases, the results are visualized through, e.g., concept maps, quadrant reports, social network maps, etc. These provide a synopsis of the literature analysis concepts and the relationships between the concepts [95]. Content analysis software tools show statistic and concept co-occurrence data, including summaries of concept and co-occurrence frequency between the concepts, offering quantitative illustrations of the prominence of particular concepts and the power of relationships between the concepts [96]. Additionally, the content data analysis tool reliably processes textual material, compared to humans, who may intuitively assign meanings to texts [97]. These demonstrate the reliability of automatic content data analysis tools [88]. In this research, we conducted the automated content analysis with _Leximancer_ software, version 5.00.140 25 August 2021, articulating the methodologies of [PERSON], [PERSON] [98], [PERSON] and [PERSON] [93; 99]. _Leximancer_ is a natural language processing software that uses \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data Collection**} & \multirow{2}{*}{**Stakeholders**} & \multicolumn{6}{c}{**Respondents per Ward**} \\ \cline{3-10}  & & **Ibeno** & **Bassi-Chache** & **Magenche** & **Bassi-Central** & **Bomorenda** & **Nyakoe** & **Totals** \\ \hline \multirow{2}{*}{Questionnaires} & Farmer & 10 & 15 & 12 & 18 & 12 & 14 & **81** \\ \cline{2-10}  & Tader & 15 & 18 & 16 & 10 & 12 & 11 & **82** \\ \hline \multirow{4}{*}{Interviews} & Agr Ext. Officers & 1 & 1 & 1 & 1 & 1 & 1 & **6** \\ \cline{2-10}  & Farmers-Group-Leaders & 2 & 2 & 2 & 2 & 2 & 2 & **12** \\ \cline{2-10}  & Traders & 3 & 2 & 2 & 3 & 2 & 3 & **15** \\ \hline \multirow{2}{*}{Focus groups} & Farmers & 1 & 1 & 1 & 1 & 1 & 1 & **6** \\ \cline{2-10}  & Traders & 1 & 1 & 1 & 1 & 1 & 1 & **6** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of data collection methods, the types of stakeholder participants and the number of respondents per ward, and the totals per collection tool utilized. Bayesian theory. The _Leximancer_ software tool applies unsupervised learning to determine the concept frequencies and their relationships.