Trending the AI landscape
Artificial intelligence skyrocketed this past year. Its influence on communities, businesses, and the world in general was unparalleled.
To better understand this landscape, we tracked AI’s evolution from last summer to this summer. We identified the top organically arising AI conversation drivers engaged by tech professionals and tech enthusiasts.
Figure 1: The chart illustrates share of volume (SoV) for the top 15 conversation drivers within the tech professionals and tech enthusiasts AI landscape from July 2022 to June 2023. The drivers are ordered by the SoV for the 12-month time frame – Deep Learning generated the highest SoV; Workforce Implications generated the least SoV. The bars cycle through SoV for Q3 2022 (Jul-Sept), Q4 2022 (Oct-Dec), Q1 2023 (Jan-Mar), and Q2 2023 (Apr-Jun). The bar labels represent the quarterly rank (of 15) for the respective AI driver. The reference line denotes median quarterly volume of conversation for the 12-month time frame.
The AI topical landscape experienced dramatic shifts. Most notably, we can see that discussion about large language models (LLMs) gained the most momentum. This probably comes as no surprise considering the immense popularity of LLMs like ChatGPT and similar generative AI tools. LLMs went from went from very low on the public’s radar last summer to being ranked 1st this summer.
Using social analytics to connect the dots How powerful would it have been to get a sense of the significance of ChatGPT before it took off?
By observing how topics interconnect with one another we can shape them into dynamic insights!
These networks allow us to examine how ideas and attitudes are connected so that we can effectively address business questions and stay ahead of trends.
Figure 2: Mapped network graph generated using Quid.
Tracking the emergent thematic life cycle
Figure 3: Quadrant legend for topic life cycle perceptual map.
The typical journey of a topic begins as niche, starts to emerge, becomes more resonant, and then continues as generally top of mind. Topics fade and revitalize within the larger network as dynamics evolve.
We can identify where these topics are within its life cycle by mapping the volume of conversation (vertical axis) by the networked connectedness of that conversation (horizontal axis).
How have our AI drivers been permeating in the landscape?
Figure 4: This chart perceptually maps the top 15 conversation drivers within the tech professionals and tech enthusiasts AI landscape for the July 2022 to June 2023 time period. The size of the bubble represents the social following (# of followers) of those posting about this topic.
The emerging quadrant in the lower right highlights topics bubbling beneath the surface. These emergent topics drive behaviors that become predominant. The more to the right a topic sits, the more topically connected it is to the other topics plotted on the map. This means the topic’s themes are permeating more throughout the greater conversation landscape.
Let’s look at AI’s Workforce Implications in the emerging quadrant. Its large bubble size indicates that those discussing this topic have relatively larger social followings, suggesting a potentially larger sphere of influence. Its positioning on the horizontal axis indicates that it has more points of semantic connection with other topics in the broader network than the topics to its left have.
AI’s impact on the workforce is the most relevant emerging topic despite being the least discussed topic! Throughline insights like these can enlighten business decisions or validate hypotheses that would not have been observed if volume, for example, were our primary analysis dimension.
Figure 5: This chart perceptually maps Large Language Models discussion for each quarter: 2022 Q3, 2022 Q4, 2023 Q1, and 2023 Q2. The size of the bubble represents the social following (# of followers) of those posting about the topic.
When we track the topic life cycle of Large Language Models (LLMs) we can more clearly see that its progression is nonlinear. By fall of last year, LLMs like ChatGPT had made gradual waves as an emerging topic. Its sizable shift to the right represents the significant changes in how the topic was being discussed. Data-driven nuance like this enables us to manage areas of interest substantially ahead of a cultural peak!
Expanding your social intelligence capability with PSB
The research questions that topic life cycle analyses can be applied to are endless! The topics could be audiences, brands, messaging, segmentations, or conceptual landscapes such as artificial intelligence!
We can isolate early signals that lead to promising white space opportunities.
This empowers you to:
Reach out to learn more about how social analytics can support your organization in futureproofing and beyond!
- Discover niche insights – stay ahead of the curve!
- Detect emerging insights – know what’s simmering before peak moments!
- Discern resonant insights – validate expectations and staying power!
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