PREDICTION IN SOCIAL MEDIA FOR MONITORING AND RECOMMENDATION

Social media including blogs and microblogs provide a rich window into user online activity. Monitoring social media datasets can be expensive due to the scale and inherent noise in such data streams. Monitoring and prediction can provide significant benefit for many applications including brand monitoring and making recommendations. Consider a focal topic and posts on multiple blog channels on this topic. Being able to target a few potentially influential blog channels which will contain relevant posts is valuable. Once these channels have been identified, a user can proactively join the conversation themselves to encourage positive word-of-mouth and to mitigate negative word-of-mouth. Links between different blog channels, and retweets and mentions between different microblog users, are a proxy of information flow and influence. When trying to monitor where information will flow and who will be influenced by a focal user, it is valuable to predict future links, retweets and mentions. Predictions of users who will post on a focal topic or who will be influenced by a focal user can yield valuable recommendations. In this thesis we address the problem of prediction in social media to select social media channels for monitoring and recommendation. Our analysis focuses on individual authors and linkers. We address a series of prediction problems including future author prediction problem and future link prediction problem in the blogosphere, as well as prediction in microblogs such as twitter. For the future author prediction in the blogosphere, where there are network properties and content properties, we develop prediction methods inspired by information retrieval approaches that use historical posts in the blog channel for prediction. We also train a ranking support vector machine (SVM) to solve the problem, considering both network properties and content properties. We identify a number of features which have impact on prediction accuracy. For the future link prediction in the blogosphere, we compare multiple link prediction methods, and show that our proposed solution which combines the network properties of the blog with content properties does better than methods which examine network properties or content properties in isolation. Most of the previous work has only looked at either one or the other. For the prediction in microblogs, where there are follower network, retweet network, and mention network, we propose a prediction model to utilize the hybrid network for prediction. In this model, we define a potential function that reflects the likelihood of a candidate user having a specific type of link to a focal user in the future and identify an optimization problem by the principle of maximum likelihood to determine the parameters in the model. We propose different approximate approaches based on the prediction model. Our approaches are demonstrated to outperform the baseline methods which only consider one network or utilize hybrid networks in a naive way. The prediction model can be applied to other similar problems where hybrid networks exist

State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work

When Is Employee Blogging Protected by Section 7 of the NLRA?

The National Labor Relations Act forbids employers from retaliating against certain types of employee speech or intimidating those who engage in it. This iBrief examines how blogging fits into the current statutory framework and recommends how the National Labor Relations Board and the courts should address the unique features of employee blogs

Forming a social media marketing strategy : Increasing product awareness and generating leads for a startup company in diving industry

Social media is used by billions of people daily to share and interact. While the main focus for many social media channels is to allow communications between individuals, companies have also noticed the vast potential social media channels have to offer. At present, many companies of varying sizes are using social media for numerous purposes, brand building, customer service and information gathering being some of the most typical. This study analyzes how social media marketing can be used to effectively promote a startup company and their product with the goal of presenting a social media marketing plan for UWIS Oy (Underwater Information Systems). UWIS is a startup company founded in Finland in 2014. They are launching a new product to completely new market within diving industry during the year 2017. Because of limited resources, UWIS has decided to use selected social media channels to raise product awareness, establish company image, manage customer relationships and gain information about the industry. In order for UWIS to achieve these goals effectively, this study examined some prominent and successful industry organizations for benchmarking purposes. A semi structured interview was conducted either via Skype or email. The interview analyzed the social media behavior of potential customers within diving industry as well as their favored content in the social media channels they use. The interviews and benchmarks derived from diving industry organizations and their activities suggest that potential customers for UWIS are people who enjoy content concerning marine life, environment and the lifestyle of diving. They prefer personal, relevant and interesting social media material that promotes the values they have and overlook promotional and impersonal messaging. The findings from benchmarks and interviews were then compared with background research and non-academic literature on social media marketing to form social media marketing plan comprising of several different important aspects that together form the social media marketing plan. The research results would indicate that the ways of effectively marketing on social media haven’t changed in the recent decade. The literature still forms a robust framework for companies to structure their social media marketing strategy on, but companies need to study their audience and potential customers in order to be able to create relevant and interesting content for them

Survey on Link Prediction and Page Ranking In Blogs S.Geetha

This paper presents a study of the various aspects of link prediction and page ranking in blogs. Social networks have taken on a new eminence from the prospect of the analysis of social networks, which is a recent area of research which grew out of the social sciences as well as the exact sciences, especially with the computing capacity for mathematical calculations and even modelling which was previously impossible. An essential element of social media, particularly blogs, is the hyperlink graph that connects various pieces of content. Link prediction has many applications, including recommending new items in online networks (e.g., products in eBay and Amazon, and friends in Face book), monitoring and preventing criminal activities in a criminal network, predicting the next web page users will visit, and complementing missing links in automatic web data crawlers. Page Rank is the technique used by Google to determine importance of page on the web. It considers all incoming links to a page as votes for Page Rank. Our findings provide an overview of social relations and we address the problem of page ranking and link prediction in networked data, which appears in many applications such as network analysis or recommended systems. Keywords- web log, social networks analysis, readership, link prediction, Page ranking. I