I actually gave a talk about automating influence on social media with what effectively amounted to chatbots, as a part of the 2016 election. Many of these factors were used, but also a few other interesting elements this article didn’t mention:
1. Geographic locales have linguistic norms that silently help in-group association. Adjusting rhetoric to these terms make people more pliable.
2. Context-aware sentiment is more impactful than general sentiment analysis. Someone may not have been a Trump supporter, but they didn’t like Hillary. This became another fulcrum.
3. Social leaders had the biggest impact, but social leaders can be made. This was leveraging a thought experiment conducted by DARPA, published in a paper titled, “Containment Control for a Social Network with State-Dependent Connectivity“ (https://arxiv.org/pdf/1402.5644.pdf)
These and a few other factors lead to my prediction of polling trajectories delivering a win for Trump in 2016 (the lecture was in August).
1. Geographic locales have linguistic norms that silently help in-group association. Adjusting rhetoric to these terms make people more pliable.
2. Context-aware sentiment is more impactful than general sentiment analysis. Someone may not have been a Trump supporter, but they didn’t like Hillary. This became another fulcrum.
3. Social leaders had the biggest impact, but social leaders can be made. This was leveraging a thought experiment conducted by DARPA, published in a paper titled, “Containment Control for a Social Network with State-Dependent Connectivity“ (https://arxiv.org/pdf/1402.5644.pdf)
These and a few other factors lead to my prediction of polling trajectories delivering a win for Trump in 2016 (the lecture was in August).