As communities grow and technology advances, planners of 鈥渟mart cities鈥 have turned to social media as an additional way of garnering residents鈥 feedback.
However, the sheer volume of content from those platforms can be a challenge for municipal officials looking for meaningful dialogue.
Researchers at the 草莓污视频导航鈥檚 are hoping to remove that barrier with the help of artificial intelligence and machine learning.
Collecting data from X (formerly Twitter) and building analysis models and visual datasets with the help of AI, Schulich master鈥檚 student Mitra Mirshafiee is looking to help decision-makers understand the emotions and context of each post.
Along with , PhD, and , PhD, Mirshafiee has been working through posts about The City of Calgary鈥檚 infrastructure and planning to identify issues and differences in opinion on what鈥檚 important.
鈥淲hat our work represents is that it鈥檚 not enough to just monitor how the physical facilities are doing,鈥 she says. 鈥淭he city should also take into account what people are concerned about, especially when it comes to long-term planning.鈥
A variety of opinions
Smart cities are described as municipalities that aim to improve quality of life for everyone by integrating technology and data solutions to improve operational efficiency and information sharing.
While Calgary is a 鈥渟mart city,鈥 its combination of the 311 phone and online system, along with focus groups, to determine what is more important to residents doesn鈥檛 capture everyone and is viewed as laborious.
鈥淭he issue with the calls is that they require too much human intervention and revolve around physical system deficiencies in the city. Hence, they don鈥檛 have enough information about the opinions of people on City plans and strategies.鈥
Mirshafiee points to younger generations, who don鈥檛 pay attention to surveys, don鈥檛 attend workshops or community meetings, or even bother to call The City to complain about programs.
Another gadget in the toolbox
In a she wrote with Barcomb and Tan, Mirshafiee indicates they looked at a variety of topics on what was at the time called Twitter related to Calgary and started narrowing down the amount of content to sort through.
She says the AI-directed filter was used to capture keywords and query definitions to help include or exclude certain pieces of information.
Mirshafiee says they then used hierarchical algorithms to identify unimportant or unnecessary posts like spam or bots, before identifying and classifying each of the topics and overarching emotions being felt.
For the analysis, she randomly chose 160 posts and manually labelled them as having an emotion of anger, joy, optimism or sadness.
The topics ranged from downtown redevelopment plans and housing prices to seniors鈥 care and even the Stampede.
鈥淔or example, when exploring the topics, we see many people complaining about the money spent on developing one specific part of the city or on special events for specific parts of the community,鈥 Mirshafiee says. 鈥淭hese are not things that they can or are willing to talk to 311 agents about.鈥
Mirshafiee says social media commentary is another tool for communities to gather feedback and shouldn鈥檛 be used to replace any of the existing strategies.
More-informed decisions
With her , Mirshafiee is hoping to connect with municipal and provincial officials to see how they might benefit from the data and the approach to collecting it.
鈥淲e saw how residents are voicing their concerns, complaints and suggestions through these comments, and knowing about these concerns may be very helpful to the local authorities as they decide what to do next,鈥 she says.
Mirshafiee says new AI technologies聽鈥斅燼s well as surveying additional social media platforms聽鈥斅爉ight also make the picture clearer for decision-makers.
For now, she is focused on building an application that will showcase the usefulness of what she has done to this point.
