Representation of online conversation topics in social media by a multi-layer network.

<p>The social network connects people. In the topic network, nodes represent hashtags that are linked when they co-occur; clusters represent topics (shown in colors). A person and a hashtag are connected when the person uses the hashtag.</p

Spearman rank correlation between the number of followers and the topical diversity of user interests as a function user activity.

<p>All shown correlation values are significant (<i>p</i> < 0.05).</p

Spearman rank correlation between content interestingness and the topical diversity of user interests as a function of how many followers users have.

<p>All shown correlation values are significant (<i>p</i> < 0.05).</p

AUC of prediction results using different adopter features within <i>t</i> early hours.

<p>† A linear combination with coefficients determined by regression fitting using least squared error.</p><p>Prediction features include the number of followers (<i>fol</i>), the number of tweets (<i>twt</i>), the diversity of topical interests of adopters (<i>H</i>₁), and the number of early adopters (<i>n</i>). The threshold is expressed as a top percentile of most popular hashtags that are deemed viral for evaluation purposes. Best results for each column are bolded.</p

Examples of connected topic clusters of related themes.

<p>Themes include: (a) news and politics, (b) sports, (c) soccer, and (d) music and entertainment. Each node represents a cluster of hashtags on the topic as labeled; the area is proportional to the number of hashtags that the topic cluster contains; the color is assigned according to the degree, so that high degree clusters are more red and low degree clusters more blue. All these examples are consistent with the existence of topic locality.</p

Examples of topic clusters in the hashtag co-occurrence network.

<p>Examples of topic clusters in the hashtag co-occurrence network.</p

AUC of prediction results using different co-tag features within <i>t</i> early hours.

<p>† A linear combination with coefficients determined by regression fitting using least squared error.</p><p>Prediction features include the number of tweets containing the co-tags (<i>T</i>), the number of co-tag adopters (<i>A</i>), the diversity of co-tags (<i>H</i>₂), and the number of observed co-tags (<i>m</i>). The threshold is expressed as a top percentile of most popular hashtags that are deemed viral for evaluation purposes. Best results for each column are bolded.</p

Basic statistics of the dataset, which is split into two periods: observation and testing.

<p>About 13% of the tweets contain hashtags.</p

Linear regression estimating how many times a user is retweeted.

<p>† Variables are normalized by <i>Z</i>-score.</p><p>*** <i>p</i> < 0.001</p><p>For efficiency, the regression is based on a random sample of 10% of the users (<i>N</i> = 2,171,624).</p

User A has diverse topical interests and user B displays more focused interests.

<p>Each connected group in the illustration corresponds to a social circle with common interests.</p