Dating and romantic relationships not only play a huge role in our personal lives but also collectively influence and shape society. Today, many romantic partnerships originate from the Internet, signifying the importance of technology and the web in modern dating. In this paper, we present a text-based computational approach for estimating the relationship compatibility of two users on social media. Unlike many previous works that propose reciprocal recommender systems for online dating websites, we devise a distant supervision heuristic to obtain real world couples from social platforms such as Twitter. Our approach, the CoupleNet is an end-to-end deep learning based estimator that analyzes the social profiles of two users and subsequently performs a similarity match between the users. Intuitively, our approach performs both user profiling and match-making within a unified end-to-end framework. CoupleNet utilizes hierarchical recurrent neural models for learning representations of user profiles and subsequently coupled attention mechanisms to fuse information aggregated from two users. To the best of our knowledge, our approach is the first data-driven deep learning approach for our novel relationship recommendation problem. We benchmark our CoupleNet against several machine learning and deep learning baselines. Experimental results show that our approach outperforms all approaches significantly in terms of precision. Qualitative analysis shows that our model is capable of also producing explainable results to users.
Finally, @cite_22 studied the impacts of relationship breakups on Twitter, revealing many crucial insights pertaining to the social and linguistic behaviour of couples that have just broken up. In order to do so, they collect likely couple pairs and monitor them over a period of time. Notably, our data collection procedure is reminscent of theirs, i.e., using keyword-based filters to find highly likely couple pairs. However, their work utilizes a second stage crowdworker based evaluation to check for breakups.
@cite_22
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We use public data from Twitter to study the breakups of the romantic relationships of 661 couples. Couples are identified through profile references such as @user1 writing “@user2 is the best boyfriend ever!!”. Using this data set we find evidence for a number of existing hypotheses describing psychological processes including (i) pre-relationship closeness being indicative of post-relationship closeness, (ii) “stonewalling”, i.e., ignoring messages by a partner, being indicative of a pending breakup, and (iii) post-breakup depression. We also observe a previously undocumented phenomenon of “batch un-friending and being un-friended” where users who break up experience sudden drops of 15-20 followers and friends. We analyze the dynamics of the behavior known as 'unfollow' in Twitter. We collected daily snapshots of the online relationships of 1.2 million Korean-speaking users for 51 days as well as all of their tweets. We found that Twitter users frequently unfollow. We then discover the major factors, including the reciprocity of the relationships, the duration of a relationship, the followees' informativeness, and the overlap of the relationships, which affect the decision to unfollow. We conduct interview with 22 Korean respondents to supplement the quantitative results. They unfollowed those who left many tweets within a short time, created tweets about uninteresting topics, or tweeted about the mundane details of their lives. To the best of our knowledge, this work is the first systematic study of the unfollow behavior in Twitter. We investigate the breaking of ties between individuals in the online social network of Twitter, a hugely popular social media service. Building on sociology concepts such as strength of ties, embeddedness, and status, we explore how network structure alone influences tie breaks - the common phenomena of an individual ceasing to "follow" another in Twitter's directed social network. We examine these relationships using a dataset of 245,586 Twitter "follow" edges, and the persistence of these edges after nine months. We show that structural properties of individuals and dyads at Time 1 have a significant effect on the existence of edges at Time 2, and connect these findings to the social theories that motivated the study. This study investigates unfollow behavior in Twitter, i.e. people removing others from their Twitter following lists. Considering the interdependency and dynamics of unfollow decisions, we use actor-oriented modeling (SIENA) to examine the impacts of reciprocity, status, embeddedness, homophily, and informativeness on tie dissolution. Focusing on ordinary users in tightly-knitted user groups, the results show that relational properties play key roles in the emergence of unfollow behavior: mutual following relations and common followees reduce the likelihood of unfollowing. And unfollow tends to be reciprocal: when a user is unfollowed by someone, he or she will unfollow back. However, there is no evidence of the impacts of homophily based on common interests and informativeness of interactions. The findings suggest that Twitter has many heterogeneous user groups and relational and informational factors may not be applicable universally.