We Know #Tag: Does the Dual Role Affect Hashtag Adoption? Lei Yang 1, Tao Sun 2, Ming Zhang 2, Qiaozhu Mei 1 1 School of Information, the University of Michigan 2 School of EECS, Peking University #France #www2012 #Lyon #In_Action

Hashtag: Content Tagging #Obama#Tax Mark Content

Hashtag: Content Tagging Browse and Retrieve

Link Relevant Topics and Events Hashtag: Content Tagging e.g., #BREAKINGNEWS: #earthquake with preliminary magnitude of 3.4 has struck 11 miles north of Indio

Hashtag =? Traditional Tag HashtagTag

The study (Starbird et al., 2011) found that According to their interview Hashtag: Another Role

A hashtag defines a virtual community of users with the same background e.g., #umich, #Microsoft with the same interests e.g., #iphone, #politics involved in the same conversation or event e.g., #www2012, #VoteForObama Hashtag: Community Participation Dual Role Content Tagging + Community Participation

Initialize a new community Or Participate a community Initialize a new community Or Participate a community Create a new bookmark Or Present interests to a topic Create a new bookmark Or Present interests to a topic A user adopts a hashtag Dual Role Hashtag Adoption Communit y Participati on Content Tagging

Dual Role Hashtag Adoption Content Tagging Community Participation Factors Hashtag Adoption

To quantify factors that affect the dual role. To test whether the proposed factors will affect the behavior of hashtag adoption. To make predictions of future adoptions of hashtags. What to do

Provided a macroscopical analysis of the dual role. Provided a foundation of the rationality of the behavior of hashtag adoption in terms of the dual role. Provided an empirical analysis of how the dual role affects the behavior of hashtag adoption. Provided a feasibility study of hashtag recommendation. Contribution

Step by Step Step 1. Quantify the factors associated with the dual role

Content Tagging Relevance to the content (e.g., adaptive filtering) Closeness to users’ personal Preference (e.g., collaborative filtering) … Community Participation Prestige of community members (e.g., preferential attachment) Influence of friends in the community (e.g., social influence) … Step 1. Factors Affecting the Dual Role

Relevance assesses the similarity between a user u and a hashtag h. Step 1. Content Role - Relevance Relevance to my interests = sim (D u, D h ) A new hashtag h D h : Tweets containing h D u : Tweets u have posted

Preference measures how close a hashtag h is tied to the personal preference of a user u. Any reasonable function f (.) introduces an instantiation of preference, such as sum, average, maximum or minimum. Step 1. Content Role - Preference My preference to h = f { sim (h, h’ ) | h’ in H } H : hashtags I have used before A new hashtag h

Prestige is one of the major factors affecting the behavior of joining communities. Any reasonable function f (.) introduces an instantiation of prestige. Step 1. Community Role - Prestige A new hashtag h Users who have adopted h Retweet network G Prestige of users in G f {prestige of u’ | u’ has used h}

Influence assesses how much a user u is influenced by its friends already in the community of hashtag h. The function f (.) can be realized as any reasonable aggregate function of all the individual influences. Step 1. Community Role - Influence Retweet network G A new hashtag h U = {friends of u who have used h and may influence u} f { influence (u, u’) | u’ in U }

Role-Specific Factors Relevance Preference Prestige Influence Role-Unspecific Factors Popularity Length Degree Freshness Activeness Role-Specific and -Unspecific Factors

Datasets DatasetTime Span# Users# Tweets Politics Dataset03/ /20101,029373,439 Stream Dataset06/ / million476 million GroupDescription P OLITICS Users in Political dataset. M OVIE Users interested in movies in Stream dataset. R ANDOM Randomly sampled users in Stream dataset.

Step by Step Step 1. Quantify the factors associated with the dual role Step 2. Correlation Analysis

The relationship between role-specific factors and users’ degree of interests in hashtags. Step 2. Correlation Analysis target factor average degree of interests … … K K Time Time Interval,, …,

Step 2. Correlation Analysis RelevancePreferencePrestigeInfluence RelevancePreferencePrestigeInfluence Stream Dataset Politics Dataset Degree of Interests

Step by Step Step 1. Quantify the factors associated with the dual role Step 2. Correlation Analysis Step 3. Regression Analysis

We want to further look for evidences of Step 3. Regression Analysis

Dependent variable : 1 / 0 indicating whether u will use h. Independent variables: one instantiation of each role- specific factor. Control Factors: five instantiations of role-unspecific factors. Logistic Regression Time Calculate independent variables Calculate dependent variable Time Interval 1Time Interval 2 Never used before

Feature Abbr. β (POLITICS | MOVIE | RANDOM) + : positive, - : negative Significance Influence + | + | +*** | *** | *** Preference + | + | +*** | *** | *** Relevance + | + | +*** | *** | *** Prestige + | + | +*** | *** | *** Popularity + | - | - | *** | *** Indegree - | - | - | *** | *** Outdegree - | - | - | ** | *** Length - | - | -*** | *** | *** N.uniTag - | + | + | *** | *** Significance at the: *** 0.01, ** 0.05, or * 0.1 level. Step 3. Regression Analysis

Step by Step Step 1. Quantify the factors associated with the dual role Step 2. Correlation Analysis Step 3. Regression Analysis Step 4. Prediction of hashtag future adoption

Feasibility study of constructing an accurate and effective hashtag prediction and recommendation system. Given a user and a hashtag, we formulate the binary classification problem as the following: Support Vector Machine Step 4. Prediction of Hashtag Adoption

Training and Test Step 4. Prediction of Hashtag Adoption Time TrainingTest Interval 1Interval 2 Interval 3Interval 4 Calculate Features Estimate Class Calculate Features Estimate Class

Systems Baseline: all role-unspecific factors Baseline + relevance / preference / prestige / influence Baseline + relevance + preference + prestige + influence Hashtag adoption in retweets and non-retweets All: all tweets NonRTs: all non-retweets RTs: all retweets Step 4. Prediction of Hashtag Adoption

GroupMeasures Accuracy (%) AllNonRTsRTs P OLITIC S (B)aseline B+Relevanc e ***74.23 ***72.53 *** B+Preferenc e ***71.17 ***67.23 *** B+Influence69.31 ***68.42 ***67.23 *** B+Prestige75.52 ***74.88 ***71.32 *** All78.25 ***78.32 ***74.93 *** Significance at the: *** 0.01, ** 0.05, or * 0.1 level. Step 4. Prediction of Hashtag Adoption Prediction Performance on P OLITICS

GroupMeasures Accuracy (%) AllNonRTsRTs M OVIE (B)aseline B+Relevanc e ***78.93 ***81.66 ** B+Preferenc e ***77.66 ***80.62 *** B+Influence79.93 ***76.89 ***81.04 *** B+Prestige74.09 ***71.57 ***74.12 *** All80.64 ***79.13 ***82.80 *** Significance at the: *** 0.01, ** 0.05, or * 0.1 level. Step 4. Prediction of Hashtag Adoption Prediction Performance on M OVIE

GroupMeasures Accuracy (%) AllNonRTsRTs R ANDOM (B)aseline B+Relevanc e ***82.64 ***84.50 *** B+Preferenc e ***79.97 ***83.39 *** B+Influence77.42 ***75.56 ***80.18 *** B+Prestige74.37 ***73.39 ***75.72 *** All84.03 ***82.45 ***85.64 *** Significance at the: *** 0.01, ** 0.05, or * 0.1 level. Step 4. Prediction of Hashtag Adoption Prediction Performance on R ANDOM

Results of analyses in this work all indicate that a hashtag serves as both a tag of content and a symbol of membership of a community. The measures we propose to quantify the factors all present significant predictive power to the adoption of hashtags. The prediction analysis provides a feasibility study of hashtag recommendation systems, suggesting a promising future direction of research. Conclusion

Study and differentiate the two roles of hashtags. Study what role users are adopting when they are adopting a new hashtag. Study how to better make use of the dual role to do hashtag recommendation. Future Work

Thanks!