Industrial technology conferences are used to demonstrate and report on the latest trends in software, systems, gadgets, frameworks, services, solutions, and more. To estimate the future impact of these trends corresponding triggered social network interactions might be a viable data source for evaluation. Trends that trigger more interactions might be more interesting to the audience than trends with less impact on social media channels.
This thesis shall exploratively gather and analyze data from the social network Twitter around cloud computing related technology conferences. It should analyze whether it is possible to identify such trends and correlated multipliers. The overall aim is to identify automatically the
in a stream of social network interactions around technology conferences. Therefore, the thesis compromises the following tasks:
Additionally, the analysis solution should demonstrate its operational functioning on two concrete case studies (technology conferences about cloud computing and container-based cloud-native platforms). The following technology conferences should be used to demonstrate the operational functioning. It should be considered that the relevant reporting around conferences already starts in the upfront and endures more extended than the events.
Both use cases are not offensive from an ethical point of view. However, the thesis should also discuss critically how the same tools and techniques could be misused. E.g., national authorities or further actors could observe systematically social media channels to suppress or manipulate public opinions or freedom of media reporting.
Note: Should individual aspects are shown to be too difficult to achieve during the processing of this work, the task can be adapted - after credible proof of difficulty.