# Framework for Social Media Analysis Based on Hashtag Research
## 3 Social Media Analysis Based on Hashtag Research Framework
### Network Visualization  
The goal of network visualization is to identify individual communities and their relative position. After importing the data into the Gephi program, the network's visualization is concentrated in the basic square, without visualizing the different relationships of individual hashtags, as seen in Figure 3. This visualization is unsatisfactory in terms of the identification of communities and their mutual position, but for the analysis of characteristics at the level of hashtags and the whole network, there is no effect. Based on the modularity analysis, individual hashtags are assigned colours corresponding to individual groups, which are extracted by this algorithm, as seen in Figure 4. It is now possible to determine the number of communities and their representation through component analysis. In terms of identifying a location of individual communities, this visualization is still insufficient. In the field of visualization, it is possible to use the following algorithms, which are part of programs for visualization and analysis of data in networks: ForceAtlas, an algorithm used for networks based on the so-called small-world network. This is a type of mathematical graph in which most nodes are not neighbours, but neighbours of any given node are likely to be neighbours of each other, and most nodes can be reached from every other node by a small number of connections [57]; ForceAtlas2, an improved version of the ForceAtlas algorithm, which focuses on large networks. It is a method based on reduced samples' visual representation to define network communities and their types [58]. The advantage over ForceAtlas is its speed and ease of computing. The ideal number of hashtags is 10,000-100,000 [59]. To visualize a network with a number of hashtags from 1000 to 10,000, it is best to use the ForceAtlas 2 algorithm due to the calculation speed. The result of the ForceAtlas 2 algorithm is, for example, a subsequent layout (see Figure 5), where it is possible to identify five main communities and their mutual position (polarity). Figure 4: Network visualization in Gephi after modularity calculation without application of algorithm for layout visualization (14,788 hashtags and 363,525 edges). Source: Own processing. Figure 3: Default network visualization after import into Gephi (14,788 hashtags and 363,525 edges). Source: Own processing. * **Data Evaluation**--the main aim is to identify specific patterns that represent knowledge based on the previous steps obtained. This is especially important in this area on the interpretation of values of the degree of hashtag, eigenvector centrality, modularity and visual assessment of the distribution of communities in the network. **Visual Assessment of the Layout of Communities in the Network**  
Based on identifying the modularity value, the hashtag degree values and visualizing the network, community identification is performed, where it is possible to identify communities characterizing a reduced sample. For this process, it is possible to use a division into three basic types of communities [58]. The identification of these communities was based on communication and forwarding messages between members. This theory contains six basic types of community. However, in the hashtag analysis, it is not possible to determine the hashtag's direction compared to other hashtags in the given message. Thus, they are selected and only two are described (see Figure 6), which are not conditioned by the direction of the relationship between the individual communities. These communities are made up of people who have replaced hashtags in the framework. Figure 5: Network hashtag layout based on the ForceAtlas 2 algorithm (1425 hashtags and 195,993 edges). Source: “Education and Business as a Key Topics at the Instagram Posts in the Area of Gamification” [20]. **Divided Communities--**polarized communities are created for situations where two groups discuss the same topic, but communication between the topics is low.