Authenticity of Geo-Location and Place Name in Tweets

The place name and geo-coordinates of tweets are supposed to represent the possible location of the user at the time of posting that tweet. However, our analysis over a large collection of tweets indicates that these fields may not give the correct location of the user at the time of posting that tweet. Our investigation reveals that the tweets posted through third party applications such as Instagram or Swarmapp contain the geo-coordinate of the user specified location, not his current location. Any place name can be entered by a user to be displayed on a tweet. It may not be same as his/her exact location. Our analysis revealed that around 12% of tweets contains place names which are different from their real location. The findings of this research can be used as caution while designing location-based services using social media

CommuniSense: Crowdsourcing Road Hazards in Nairobi

Nairobi is one of the fastest growing metropolitan cities and a major business and technology powerhouse in Africa. However, Nairobi currently lacks monitoring technologies to obtain reliable data on traffic and road infrastructure conditions. In this paper, we investigate the use of mobile crowdsourcing as means to gather and document Nairobi's road quality information. We first present the key findings of a city-wide road quality survey about the perception of existing road quality conditions in Nairobi. Based on the survey's findings, we then developed a mobile crowdsourcing application, called CommuniSense, to collect road quality data. The application serves as a tool for users to locate, describe, and photograph road hazards. We tested our application through a two-week field study amongst 30 participants to document various forms of road hazards from different areas in Nairobi. To verify the authenticity of user-contributed reports from our field study, we proposed to use online crowdsourcing using Amazon's Mechanical Turk (MTurk) to verify whether submitted reports indeed depict road hazards. We found 92% of user-submitted reports to match the MTurkers judgements. While our prototype was designed and tested on a specific city, our methodology is applicable to other developing cities.Comment: In Proceedings of 17th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI 2015

Identifying and verifying news through social media: Developing a user-centred tool for professional journalists

Identifying and verifying new information quickly are key issues for journalists who use social media. This article examines what tools journalists think they need to cope with the growing volume and complexity of news on social media, and what improvements are needed in existing systems. It gives some initial results from a major EU research project (Social Sensor), involving computer scientists, journalists, and media researchers, that is designing a new tool to search across social media for news stories, to surface trends, and to help with verification. Preliminary results suggest that an effective tool should focus on the role of key influencers, and should be customisable to suit the particular needs of individual journalists and news organisations

@yourlocation: A Spatial Analysis of Geotagged Tweets in the US

This project examines the spatial network properties observable from geo-located tweet data. Conventional exploration examines characteristics of a variety of network attributes, but few employ spatial edge correlations in their analysis. Recent studies have demonstrated the improvements that these correlations contribute to drawing conclusions about network structure. This thesis expands upon social network research utilizing spatial edge correlations and presents processing and formatting techniques for JSON (JavaScript Object Notation) data

Migrations

Moving to a new place can often be difficult and uncomfortable. I would like to create an interactive installation which uses live tweets to show instances of relocation and immigration throughout the world. If successful, the project will allow viewers to feel some kind of connection between their own experiences and the stories shown in the installation.https://remix.berklee.edu/graduate-studies-production-technology/1060/thumbnail.jp

Location Reference Recognition from Texts: A Survey and Comparison

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs

The Impact of Twitter Features on Credibility Ratings - An Explorative Examination Combining Psychological Measurements and Feature Based Selection Methods

In a post-truth age determined by Social Media channels providing large amounts of information of questionable credibility while at the same time people increasingly tend to rely on online information, the ability to detect whether content is believable is developing into an important challenge. Most of the work in that field suggested automated approaches to perform binary classification to determine information veracity. Recipients´ perspectives and multidimensional psychological credibility measurements have rarely been considered. To fill this gap and gain more insights into the impact of a tweet´s features on perceived credibility, we conducted a survey asking participants (N=2626) to rate the credibility of crises related tweets. The resulting 24.823 ratings were used for an explorative feature selection analysis revealing that mostly meta-related features like the number of followers of the author, the count of tweets produced and the ratio of tweet number and days since account creation affect credibility judgements