Mapping Social Media


Social Media applications continue to grow in popularity for both individuals and businesses.  Facebook just posted record quarterly earnings, Snap, Instagram, Twitter continue to grow and new players attempt to move into the market with each passing week.  We are a social society posting activities, information, and opinions where ever we are.  With this comes the other side of all that posting — big data for analytics and insights.  While some platforms may be limited to public utilization, others are open to the developer community to provide detailed insights into their vast troves of data.

The visualization of social media data provides insights into a highly mobile segment of the population. Knowing where people typically post and that the typical geotagged posts with GPS are from mobile devices provides insight into where MNOs need to have the best network to serve them. Twitter being the most openly available social media data provides millions of potential records daily with location data available for use. Other platforms allow similar retrieval of this critical information that might be used by Mobile Network Operators (MNOs) to validate capacity plans, network coverage densification, and areas of targeted growth for improved networks. The data also helps justify store locations and traffic modeling of past events to predict traffic loading for things such as sporting events, major concerts and festivals, in addition to known community events.

One of the greatest early use cases in current networks is small cells.  With small cells mostly having a small coverage radius, it is critical for MNOs to place them in the correct location for traffic offload, interference mitigation, and valued coverage.  Geolocation data has long been the desired focus in wireless, the issue continues to be the ability of algorithms to provide accurate data from network measurements, versus crowd sourced data with accurate GPS.  Knowing where customer (or potential customers) are located and using networks makes small cell placement in conjunction with the existing network efficient and allow the best return on investment.  The image above shows the potential value of placing two small cells with offload capabilities versus two that may provide minimal.  This quick validation analysis ensure capital expenditures are in the proper places.

Social media mapping has limitations in total quantity of data available from all platforms. However, it provides the samples in urban areas where often the spectrum challenges are the greatest. 5G deployments and the ongoing LTE densification projects will continue to push the value for getting network capacity that improves the network rather than just reducing spectrum utilization through interference.

The next step is to move beyond layers for consideration and on to automated analytics. These user locations may be inputs for machine learning against known data points in a wireless network. This could automate network capacity expansion, parameter tuning, and network configurations into real time performance as an outside data point to MNOs use of network generated KPIs.  Continued work in the application and expanded value mapped social media data can provide to MNOs.

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