Predicting tie strength with social media

By Eric Gilbert, K. G. Karahalios. 2009

1,483 citations recorded by the source corpus.

Subfields: Communication, Statistical and Nonlinear Physics

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In Communication

In Statistical and Nonlinear Physics

Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Further, the type and the dynamics of offline relationships influence users propensity to create social ties and interact online (Subrahmanyam et al., 2008; Gilbert and Karahalios, 2009).”
Predicting Personality from Twitter (2011, methodology): “Following the methods used in [26], [36] as well as other studies of social media behavior, such as [13], we utilized two main tools to analyze the content of users' tweets.”
SybilSCAR: Sybil detection in online social networks via local rule based propagation (2017, background): “One way is to approximately obtain trust relationships between users by looking into user interactions [32], predicting tie strength [33], asking users to rate their social contacts [34], etc.”
Growing closer on facebook: changes in tie strength through social network site use (2014, background): “That people have very strong relationships online is not in dispute; 40% of SNS users have “friended” all of their closest offline confidants online [15], and tie strength can be inferred from site use [14,24].”
What's Different about Social Media Networks? A Framework and Research Agenda (2014, background): “However, the relational formalism of social media platforms also limits relational capabilities, such as by limiting the amount of nuance people can attribute to labels such as " friend " or " follower " (Gilbert and Karahalios 2009).”
It's not that i don't have problems, i'm just not putting them on facebook: challenges and opportunities in using online social networks for health (2011, background): “explored by Gilbert and Karahalios [9], though it is not clear that their approach of partitioning contacts into “strong” and “weak” ties will be adequate for supporting health-related communication, which our study suggests would require partitions along multiple dimensions.”
Exploiting Temporal Dynamics in Sybil Defenses (2015, background): “The key insight in these defenses is that it is hard for an adversary to set up trust relationships with honest users (called attack edges), particularly when user interactions are used to infer strong social ties [11, 31] (see Section 2).”
Privacy wizards for social networking sites (2010, background): “For example, it would be interesting to understand whether ideas such as tie strength [20] are useful”
Computational Sociolinguistics: A Survey (2016, background): “Gilbert and Karahalios (2009) explored how different types of information (including messages posted) can be used to predict tie strength on Facebook.”
Depression detection from social network data using machine learning techniques (2018, background): “…such as Decision trees —Simple DT, Medium DT, and Complex DT; SVM —Linear, Quadratic, Cubic, Fine Gaussian, Medium Gaussian, and Coarse Gaussian; KNN —Fine, Medium, Coarse, Cosine, Cubic and Weighted, Ensemble —Boosted tree, Bagged tree, Subspace discriminant, Subspace KNN, RUSBoosted Tree [42–44].”
Social network activity and social well-being (2010, background): “Wall posts and messages are strong predictors of relationship strength [5], and one-on-one chat sessions are associated with reduced loneliness and depression [13].”
Recommendations in location-based social networks: a survey (2015, background): “User recommendations, which includes popular user discovery [12, 38, 98], friend recommendation [5, 16, 82, 104, 117], and community discovery [60, 103], have been extensively studied in the context of traditional social networks.”
Social Recommendation with Strong and Weak Ties (2016, methodology): “There has been recent work in Computer Science that pays attention to tie strength in social media [4–8, 20, 40, 45] and with demographic data [9].”
Social Media Ethnography: The Digital Researcher in a Messy Web (2012, background): “, 2010) and social network analysis (Gilbert and Karahalios, 2009; Java et al., 2007; Prieur et al., 2009).”