Geotag propagation in social networks based on user trust model

In the past few years sharing photos within social networks has become very popular. In order to make these huge collections easier to explore, images are usually tagged with representative keywords such as persons, events, objects, and locations. In order to speed up the time consuming tag annotation process, tags can be propagated based on the similarity between image content and context. In this paper, we present a system for efficient geotag propagation based on a combination of object duplicate detection and user trust modeling. The geotags are propagated by training a graph based object model for each of the landmarks on a small tagged image set and finding its duplicates within a large untagged image set. Based on the established correspondences between these two image sets and the reliability of the user, tags are propagated from the tagged to the untagged images. The user trust modeling reduces the risk of propagating wrong tags caused by spamming or faulty annotation. The effectiveness of the proposed method is demonstrated through a set of experiments on an image database containing various landmark

The Paradox of Social Media Security: A Study of IT Students’ Perceptions versus Behavior on Using Facebook

Social media plays an essential role in the modern society, enabling people to be better connected to each other and creating new opportunities for businesses. At the same time, social networking sites have become major targets for cyber-security attacks due to their massive user base. Many studies investigated the security vulnerabilities and privacy issues of social networking sites and made recommendations on how to mitigate security risks. Users are an integral part of any security mix. In this thesis, we explore the relationship between users’ security perceptions and their actual behavior on social networking sites. Protection motivation theory (PMT), initially developed to study fear appeals, has been widely used to examine people’s behavior in information security domains. We propose that PMT theory can also be adapted to explain and predict social media users’ behaviors that have security implications. We use a web-based survey to measure users’ security awareness on social networking sites and collect data on their actual behavior

The network structure of visited locations according to geotagged social media photos

Businesses, tourism attractions, public transportation hubs and other points of interest are not isolated but part of a collaborative system. Making such collaborative network surface is not always an easy task. The existence of data-rich environments can assist in the reconstruction of collaborative networks. They shed light into how their members operate and reveal a potential for value creation via collaborative approaches. Social media data are an example of a means to accomplish this task. In this paper, we reconstruct a network of tourist locations using fine-grained data from Flickr, an online community for photo sharing. We have used a publicly available set of Flickr data provided by Yahoo! Labs. To analyse the complex structure of tourism systems, we have reconstructed a network of visited locations in Europe, resulting in around 180,000 vertices and over 32 million edges. An analysis of the resulting network properties reveals its complex structure.Comment: 8 pages, 3 figure

Geotag Propagation in Social Networks Based on User Trust Model

In the past few years sharing photos within social networks has become very popular. In order to make these huge collections easier to explore, images are usually tagged with representative keywords such as persons, events, objects, and locations. In order to speed up the time consuming tag annotation process, tags can be propagated based on the similarity between image content and context. In this paper, we present a system for efficient geotag propagation based on a combination of object duplicate detection and user trust modeling. The geotags are propagated by training a graph based object model for each of the landmarks on a small tagged image set and finding its duplicates within a large untagged image set. Based on the established correspondences between these two image sets and the reliability of the user, tags are propagated from the tagged to the untagged images. The user trust modeling reduces the risk of propagating wrong tags caused by spamming or faulty annotation. The effectiveness of the proposed method is demonstrated through a set of experiments on an image database containing various landmarks

Data Census of a Geographically-Bounded Tweet Set to Enhance Common Operational Picture Tools

Location information is of particular importance to crisis informatics. The Twitter API provides several methods to assess a rough location and/or the specific latitude and longitude in which a post originated. This paper offers a comparison of location information provided by Twitter’s four geolocation methods. The study aggregates one month of data from the greater Cincinnati, Ohio metropolitan area and assesses the relative contribution that each method can make to common operational picture tools used by crisis informatics researchers. Results show that of 49,744 Tweets, 4% contained geotags, 85.2% contained a location in the users’ profile, and 3.5% contained no apparent location data, but were gathered using the bounding box method and would not have been identified using traditional methods of gathering data using geotagged Tweets or user profile information alone. We reflect on these results in light of design implications for common operational picture tools (COPs)

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

In Tags We Trust: Trust modeling in social tagging of multimedia content

Tagging in online social networks is very popular these days, as it facilitates search and retrieval of multimedia content. However, noisy and spam annotations often make it difficult to perform an efficient search. Users may make mistakes in tagging and irrelevant tags and content may be maliciously added for advertisement or self-promotion. This article surveys recent advances in techniques for combatting such noise and spam in social tagging. We classify the state-of-the-art approaches into a few categories and study representative examples in each. We also qualitatively compare and contrast them and outline open issues for future research