This network visualization shows the connections between different topics mentioned in the AI Open Concerns responses. Larger nodes represent more frequently mentioned topics, and thicker edges indicate topics that frequently co-occur.

These metrics quantify various properties of the topics in the network of AI societal concerns, including their frequency and centrality measures.

This chart shows the most frequently mentioned topics in the AI Open Concerns responses.

These wordclouds show the most common terms within each topic from the AI Open Concerns responses.

This network visualization shows the connections between different topics mentioned in the Bot Factors responses. Larger nodes represent more frequently mentioned topics, and thicker edges indicate topics that frequently co-occur.

These metrics quantify various properties of the topics about what factors made profiles seem more bot-like, including their frequency and centrality measures.

This chart shows the most frequently mentioned topics in the Bot Factors responses.

These wordclouds show the most common terms within each topic from the Bot Factors responses.

This visualization highlights differences in AI concern patterns between male and female respondents. Blue nodes are topics more commonly mentioned by males, and red nodes are more commonly mentioned by females.

This chart shows the topics with the largest differences in usage between male and female respondents.

This visualization highlights differences in bot factor patterns between male and female respondents.

This chart shows the bot factors with the largest differences in usage between male and female respondents.

This visualization highlights differences in AI concern patterns between white and Black respondents.

This chart shows the topics with the largest differences in usage between racial groups.

This visualization highlights differences in bot factor patterns between white and Black respondents.

This chart shows the bot factors with the largest differences in usage between racial groups.

This visualization highlights differences in AI concern patterns between Democrat and Republican respondents. Blue nodes are topics more commonly mentioned by Democrats, and red nodes are more commonly mentioned by Republicans.

This chart shows the topics with the largest differences in usage between Democrat and Republican respondents.

This visualization highlights differences in bot factor patterns between Democrat and Republican respondents. Blue nodes are factors more commonly mentioned by Democrats, and red nodes are more commonly mentioned by Republicans.

This chart shows the bot factors with the largest differences in usage between Democrat and Republican respondents.

This visualization highlights differences in AI concern patterns between male and female respondents. Blue nodes are topics more commonly mentioned by males, and red nodes are more commonly mentioned by females.

This chart shows the topics with the largest differences in usage between male and female respondents.

This visualization highlights differences in bot factor patterns between male and female respondents.

This chart shows the bot factors with the largest differences in usage between male and female respondents.

This visualization highlights differences in AI concern patterns between white and Black respondents.

This chart shows the topics with the largest differences in usage between racial groups.

This visualization highlights differences in bot factor patterns between white and Black respondents.

This chart shows the bot factors with the largest differences in usage between racial groups.

The number of times a topic was mentioned across all responses. This is the most basic measure of a topic's prevalence.

In the visualizations, node size is typically proportional to frequency.

Measures how connected a topic is to other topics. It's calculated as the number of connections (edges) a topic has, divided by the maximum possible connections.

Topics with high degree centrality act as "hubs" in the conversation, connecting to many other topics.

Measures how often a topic serves as a bridge between other topics. It's calculated based on the number of shortest paths between other topics that pass through a given topic.

Topics with high betweenness centrality often connect different clusters or themes in the conversation.

Measures how close a topic is to all other topics in the network. It's calculated as the reciprocal of the sum of the shortest path distances from a topic to all other topics.

Topics with high closeness centrality can quickly "reach" or relate to many other topics in the conversation.

Measures how connected a topic is to other well-connected topics. Unlike degree centrality, which just counts connections, eigenvector centrality considers the quality of those connections.

Topics with high eigenvector centrality are connected to topics that are themselves well-connected, indicating influence within influential clusters.