Real Time Web

Recently, all major search engines introduced a new feature: real-time search results, embedded in the first page of organic search results. The content appearing in these results is pulled within minutes of its generation from the so-called "real-time Web" such as Twitter, blogs, and news websites. In this paper, we argue that in the context of political speech, this feature provides disproportionate exposure to personal opinions, fabricated content, unverified events, lies and misrepresentations that otherwise would not find their way in the first page, giving them the opportunity to spread virally. To support our argument we provide concrete evidence from the recent Massachusetts (MA) senate race between Martha Coakley and Scott Brown, analyzing political community behavior on Twitter. In the process, we analyze the Twitter activity of those involved in exchanging messages, and we find that it is possible to predict their political orientation and detect attacks launched on Twitter, based on behavioral patterns of activity.

On Twitter, social capitalists use dedicated hashtags and mutual subscriptions to each other in order to gain followers and to be retweeted. Their methods are successful enough to make them appear as influent users. Indeed, applications dedicated to the influence measurement such as Klout and Kred give high scores to most of these users. Meanwhile, their high number of retweets and followers are not due to the relevance of the content they tweet, but to their social capitalism techniques. In order to be able to detect these users, we train a classifier using a dataset of social capitalists and regular users. We then implement this classifier in a web application that we call DDP. DDP allows users to test whether a Twitter account is a social capitalist or not and to visualize the data we use to make the prediction. DDP allows administrator to crawl data from a lot of users automatically. Furthermore, administrators can manually label Twitter accounts as social capitalists or regular users to add them into the dataset. Finally, administrators can train new classifiers in order to take into account the new Twitter accounts added to the dataset, and thus making evolve the classifier with these new recently collected data. The web application is thus a way to collect data, make evolve the knowledge about social capitalists and to keep detecting them efficiently.

The so-called real-time web (RTW) is a web of opinions, comments, and personal viewpoints, often expressed in the form of short, 140-character text messages providing abbreviated and personalized commentary in real-time. Twitter is undoubtedly the king of the RTW. It boasts 100+ million users and generates in the region of 50m tweets per day. This RTW data is far from the structured data (ratings, product features, etc.) familiar to recommender systems research, but it is useful to consider its applicability to recommendation scenarios. In this short paper we describe an experiment to look at harnessing the real-time opinions of movie fans, expressed through the Twitter-like short textual reviews available on the Blippr service (www.blippr.com). In particular we describe how users and movies can be represented from the terms used in their associated reviews and describe a number of experiments to highlight the recommendation potential of this RTW data-source and approach.

Knowledge-Based Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was

User-generated content has dominated the web's recent growth and today the so-called real-time web provides us with unprecedented access to the real-time opinions, views, and ratings of millions of users. For example, Twitter's 200m+ users are generating in the region of 1000+ tweets per second. In this work, we propose that this data can be harnessed as a useful source of recommendation knowledge. We describe a social news service called Buzzer that is capable of adapting to the conversations that are taking place on Twitter to ranking personal RSS subscriptions. This is achieved by a content-based approach of mining trending terms from both the public Twitter timeline and from the timeline of tweets published by a user's own Twitter friend subscriptions. We also present results of a live-user evaluation which demonstrates how these ranking strategies can add better item filtering and discovery value to conventional recency-based RSS ranking techniques.

In this work, we present the Klout Score, an influence scoring system that assigns scores to 750 million users across 9 different social networks on a daily basis. We propose a hierarchical framework for generating an influence score for each user, by incorporating information for the user from multiple networks and communities. Over 3600 features that capture signals of influential interactions are aggregated across multiple dimensions for each user. The features are scalably generated by processing over 45 billion interactions from social networks every day, as well as by incorporating factors that indicate real world influence. Supervised models trained from labeled data determine the weights for features, and the final Klout Score is obtained by hierarchically combining communities and networks. We validate the correctness of the score by showing that users with higher scores are able to spread information more effectively in a network. Finally, we use several comparisons to other ranking systems to show that highly influential and recognizable users across different domains have high Klout scores.

This paper takes tools of self-valuation in social media as an empirical focus. By way of a case-study of Klout, an influential measure of influence, we suggest that the forms of reactivity and self-fulfilling prophecy that have been identified as a problem with some forms of measurement are actually an intentional effect of such tools: that is, the measurements that such tools produce are not designed to capture a separate reality, but are deliberately employed to modify the activity that they themselves invite. In other words, they expect and exploit reactivity. We suggest that such media are indicative of the rise in what might be called participative metrics of value. We further suggest that the capacity to evaluate and modify the self that Klout affords is intricately tied up with the agency and (self-)valuation of Klout as a tool itself. An intermediate layer of the argument is that this tying up is achieved through the production of numbers as specific kinds of ‘enumerated entities’. We use this term to draw attention to the ways in which numbers are never simply abstractions, but always have specific material-semiotic properties. In this case, we show that these properties are tied to the use of media-specific operations, and that these properties, including those of inclusion and belonging, inform how Klout participates in particular kinds of ordering and valuation. We thus explore the interlinked movement of numbers, media, and value in social media as a kind of dynamic assemblage.

The content in online communities has become strategically important that the identification of the influential members can benefit all in developing business opportunities, forging political agendas, discussing social and societal issues, and can lead to many interesting innovative applications. Among social networking websites, one of the most important is Twitter. We are interested in identifying the topic-specific influential members in Twitter. Influence of a user may be determined by many factors. The objective of the study is to identify the factors that play a crucial role in the measurement of a Twitter user's influence, examine how the different factors impact the influence ranking and how these factors can be expressed in a mathematical model. We devise a model to score the strength of a user's influence using basic user features collected for a group of users discussing a common issue. We then validate our results using Klout as a reference point, and then further verify our ranking of the users by manual evaluation.

People’s influence has been the subject of study of
several social and humanities disciplines. Lately,
the study of user’s influence in micro-blogging platforms
arises as an important issue. Although social
influence or prestige can be defined as the potential
or ability of an individual to engage others in a
certain act, or to induce others to behave in a particular
manner, there is no global consensus on what
means to be an influential user. This work aims
at shedding some light on how to assess user in-
fluence by proposing a novel metric of user influence
based on analysing user behaviour regarding
both content-based and topological factors. The
metric does not only consider each user individually,
but also aims at assessing the interactions with
his/her neighbourhood. The statistical analysis performed
confirmed that only analysing the topological
factors is not sufficient for accurately assessing
the influence of users. Instead the published
content and its influence over the neighbourhood of
users has to be also analysed. A comparison with
a human assessment of user influence showed that
the factors considered by the proposed metric are
truly relevant for assessing people’s influence.

Social influence is a measure of how people, directly or indirectly, effect the thoughts, feelings and actions of others. As in various environments different possibilities for interactions among people exist, the definition and calculation of social influence vary as well. In this paper, we study social influence in the environment of the "modern information and communication technology (ICT) user", a smartphone user with numerous online accounts, using a plethora of services such as telco services, social networking services and various other Internet services. Original scientific contribution of this paper includes both definition of social influence in the context of modern ICT user as well as an algorithm for calculating modern ICT user's social influence. Finally, preliminary experiment of calculating user's social influence on 123 modern ICT users will be presented, as well as results discussed.

This paper focuses on the increasing prevalence of devices for self-evaluation in the context of social media. It suggests that self-measurement - i.e. aggregates of user activities, received likes, comments, retweets and other algorithmic indicators of reputation and influence - patterns and presents user data in new ways to act upon and modify the self. Furthermore, the technologies involved in self-evaluation are not designed to merely capture user engagement with social media, but act as framing devices, making certain types of insight and action possible while ruling out others. In this sense, self-evaluation can be understood as part of recommendation and premediation culture online and as devices to curate and organise streams of real-time web content.

Social media platforms like Twitter and Facebook are based on the continuous engagement of users in activities made possible through their software design and so far these platforms have only offered limited possibilities for users to access and make sense of data they generate in interaction with their contacts. But with the proliferation of user actions, especially Facebook has started to introduce devices that increasingly aggregate and cluster user activities into stories, ranks or numbers. Focusing on Facebook and Twitter, the paper will trace modes of self-evaluation both emerging within social media and from third-party plug-ins, apps and services build on top of platforms.

Special attention will be paid to so-called influence measurement devices, most particularly the Klout rank, that attempt to evaluate users’ engagement with and impact on their network. The analysis will explore how the Klout rank encourages users to act on their data and will reflect on the role of numbers, more specifically counts and rankings, seeking to describe them as media of self-evaluation. It will unfold the affective states involved in rankings and will draw on mathematical concepts of ordinality to reflect on their performative capacities. Finally, the paper will position the emergence of self-evaluative media in the context of recommendation cultures, indicating that it not only enables users to transform the self based on data, but is embedded in corporate attempts to channel user engagement into particular directions. In this sense, the investigation of self-evaluative media will draw attention to issues of temporality, numbering and measurement in social media platforms while singling out new modes of pre-structuring user activities through devices.