text: The advent of social media has provided data and insights about how people relate to information and culture. While information is composed by bits and its fundamental building bricks are relatively well understood, the same cannot be said for culture. The fundamental cultural unit has been defined as a "meme". Memes are defined in literature as specific fundamental cultural traits, that are floating in their environment together. Just like genes carried by bodies, memes are carried by cultural manifestations like songs, buildings or pictures. Memes are studied in their competition for being successfully passed from one generation of minds to another, in different ways. In this paper we choose an empirical approach to the study of memes. We downloaded data about memes from a well-known website hosting hundreds of different memes and thousands of their implementations. From this data, we empirically describe the behavior of these memes. We statistically describe meme occurrences in our dataset and we delineate their fundamental traits, along with those traits that make them more or less apt to be successful.
  @cite_18: In this paper we introduce the task of "tweet recommendation", the problem of suggesting tweets that match a user's interests and likes. We propose an Information-Retrieval-like model that leverages the content of the user's tweets and those of her friends, and that effectively retrieves a set of tweets that is personalized and varied in nature. Our approach could be easily leveraged to build, for example, a Twitter or Facebook timeline that collects messages that are of interest for the user, but that are not posted by her friends. We compare to typical approaches used in similar tasks, reporting significant gains in terms of overall precision, up to about +20 , on both a corpus-based evaluation and real world user study.
  @cite_3: As online social networks expand their role beyond maintaining existing relationships, they may look to more faceted ratings to support the formation of new connections between their users. Our study focuses on one community employing faceted ratings, CouchSurfing.org, and combines data analysis of ratings, a large-scale survey, and in-depth interviews. In order to understand the ratings, we revisit the notions of friendship and trust and uncover an asymmetry: close friendship includes trust, but high levels of trust can be achieved without close friendship. To users, providing faceted ratings presents challenges, including differentiating and quantifying inherently subjective feelings such as friendship and trust, concern over a friend’s reaction to a rating, and knowledge of how ratings can affect others’ reputations. One consequence of these issues is the near absence of negative feedback, even though a small portion of actual experiences and privately held ratings are negative. We show how users take this into account when formulating and interpreting ratings, and discuss designs that could encourage more balanced feedback.
  @cite_19: The prevalent use of social media produces mountains of unlabeled, high-dimensional data. Feature selection has been shown effective in dealing with high-dimensional data for efficient data mining. Feature selection for unlabeled data remains a challenging task due to the absence of label information by which the feature relevance can be assessed. The unique characteristics of social media data further complicate the already challenging problem of unsupervised feature selection, (e.g., part of social media data is linked, which makes invalid the independent and identically distributed assumption), bringing about new challenges to traditional unsupervised feature selection algorithms. In this paper, we study the differences between social media data and traditional attribute-value data, investigate if the relations revealed in linked data can be used to help select relevant features, and propose a novel unsupervised feature selection framework, LUFS, for linked social media data. We perform experiments with real-world social media datasets to evaluate the effectiveness of the proposed framework and probe the working of its key components.
  @cite_13: Social media profiles are telling examples of the everyday need for disclosure and concealment. The balance between concealment and disclosure varies across individuals, and personality traits might partly explain this variability. Experimental findings on the relationship between information disclosure and personality have been so far inconsistent. We thus study this relationship anew with 1,313 Facebook users in the United States using two personality tests: the big five personality test and the self-monitoring test. We model the process of information disclosure in a principled way using Item Response Theory and correlate the resulting user disclosure scores with personality traits. We find a correlation with the trait of Openness and observe gender effects, in that, men and women share equal amount of private information, but men tend to make it more publicly available, well beyond their social circles. Interestingly, geographic (e.g., residence, hometown) and work-related information is used as relationship currency, in that, it is selectively shared with social contacts and is rarely shared with the Facebook community at large.
  @cite_20: The way in which social conventions emerge in communities has been of interest to social scientists for decades. Here we report on the emergence of a particular social convention on Twitter—the way to indicate a tweet is being reposted and to attribute the content to its source. Initially, different variations were invented and spread through the Twitter network. The inventors and early adopters were well-connected, active, core members of the Twitter community. The diffusion networks of these conventions were dense and highly clustered, so no single user was critical to the adoption of the conventions. Despite being invented at different times and having different adoption rates, only two variations came to be widely adopted. In this paper we describe this process in detail, highlighting insights and raising questions about how social conventions emerge.
summary: Internet memes spread over social media websites. In computer science, many researchers have addressed the problem of describing the dynamics of user behavior in social media websites. The topics touched include the emergence of conventions in online social networks @cite_20 , how to select the critical features in the amount of data generated in these websites @cite_19 , the privacy aspects @cite_13 , the follow'' and friendship dynamics in Twitter @cite_18 and @cite_3 , and many more.