Providing awareness, explanation and control of personalized filtering in a social networking site

Social networking sites (SNSs) have applied personalized filtering to deal with overwhelmingly irrelevant social data. However, due to the focus of accuracy, the personalized filtering often leads to “the filter bubble” problem where the users can only receive information that matches their pre-stated preferences but fail to be exposed to new topics. Moreover, these SNSs are black boxes, providing no transparency for the user about how the filtering mechanism decides what is to be shown in the activity stream. As a result, the user’s usage experience and trust in the system can decline. This paper presents an interactive method to visualize the personalized filtering in SNSs. The proposed visualization helps to create awareness, explanation, and control of personalized filtering to alleviate the “filter bubble” problem and increase the users’ trust in the system. Three user evaluations are presented. The results show that users have a good understanding about the filter bubble visualization, and the visualization can increase users’ awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over the data stream they are seeing. The intuitiveness of the design is overall good, but a context sensitive help is also preferred. Moreover, the visualization can provide users with better usage experience and increase users’ trust in the system

Providing awareness, explanation and control of personalized stream filtering in a P2P social network

In Online Social Networks (OSNs), users are often overwhelmed with a huge amount of social data, most of which are irrelevant to their interest. Filtering of the social data stream is the common way to deal with this problem, and it has already been applied by OSNs, such as Facebook and Google+. Unfortunately, personalized filtering leads to “the filter bubble” problem where the user is trapped inside a world within the limited boundaries of her interests and cannot be exposed to any surprising, desirable information. Moreover, these OSNs are black boxes, providing no transparency for the user about how the filtering mechanism decides what is to be shown in the activity stream. As a result, the user trust in the system can decline. This thesis presents an interactive method to visualize the personalized stream filtering in OSNs. The proposed visualization helps to create awareness, explanation, and control of personalized stream filtering to alleviate “the filter bubble” problem and increase the users’ trust in the system. The visualization is implemented in MADMICA – a new privacy-aware decentralized OSN, based on the Friendica P2P protocol, which filters the social updates stream of users based on their interests. The results of three user evaluations are presented in this thesis: small-scale pilot study, qualitative study and large-scale quantitative study with 326 participants. The results of the small-scale study show that the filter bubble visualization makes the users aware of the filtering mechanism, engages them in actions to correct and change it, and as a result, increases the users’ trust in the system. The qualitative study reveals a generally higher proportion of desirable user perceptions for the awareness, explanation and control of the filter bubble provided by the visualization. Moreover, the results of the quantitative study demonstrate that the visualization leads to increased users’ awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over the data stream they are seeing

Countering Personalized Speech

Social media platforms use personalization algorithms to make content curation decisions for each end user. These personalized recommendation decisions are essentially speech conveying a platform\u27s predictions on content relevance for each end user. Yet, they are causing some of the worst problems on the internet. First, they facilitate the precipitous spread of mis- and disinformation by exploiting the very same biases and insecurities that drive end user engagement with such content. Second, they exacerbate social media addiction and related mental health harms by leveraging users\u27 affective needs to drive engagement to greater and greater heights. Lastly, they erode end user privacy and autonomy as both sources and incentives for data collection. As with any harmful speech, the solution is often counterspeech. Free speech jurisprudence considers counterspeech the most speech-protective weapon to combat false or harmful speech. Thus, to combat problematic recommendation decisions, social media platforms, policymakers, and other stakeholders should embolden end users to use counterspeech to reduce the harmful effects of platform personalization. One way to implement this solution is through end user personalization inputs. These inputs reflect end user expression about a platform\u27s recommendation decisions. However, industry-standard personalization inputs are failing to provide effective countermeasures against problematic recommendation decisions. On most, if not all, major social media platforms, the existing inputs confer limited ex post control over the platform\u27s recommendation decisions. In order for end user personalization to achieve the promise of counterspeech, I make several proposals along key regulatory modalities, including revising the architecture of personalization inputs to confer robust ex ante capabilities that filter by content type and characteristics

The effect of privacy policies on information sharing behavior on social networks: A Systematic Literature Review

Online social networks (OSN) such as Facebook and Instagram have dramatically changed the way people operate. It, however, raises specific privacy concerns due to their inherent handling of personal data. The paper highlights the privacy concerns associated with OSN, strategies to protect the users’ privacy, and finally the overall effect of privacy policies on information sharing behavior on OSN. We examined a sample of 51 full papers that explore privacy concerns in OSN, strategies to protect users’ privacy, and the effects of privacy policies on the users’ information sharing behavior. The overall findings disclosed that users are concerned about their identity being stolen, and how third-party applications use their information. However, privacy policies do not have a direct impact on the information sharing behavior of OSN users. The findings help researchers and practitioners better understand the impact of privacy concern on users\u27 information sharing behaviors on OSN

The influence of national culture on the attitude towards mobile recommender systems

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. 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 submitted for publication. Copyright @ 2013 Elsevier B.V.This study aimed to identify factors that influence user attitudes towards mobile recommender systems and to examine how these factors interact with cultural values to affect attitudes towards this technology. Based on the theory of reasoned action, belief factors for mobile recommender systems are identified in three dimensions: functional, contextual, and social. Hypotheses explaining different impacts of cultural values on the factors affecting attitudes were also proposed. The research model was tested based on data collected in China, South Korea, and the United Kingdom. Findings indicate that functional and social factors have significant impacts on user attitudes towards mobile recommender systems. The relationships between belief factors and attitudes are moderated by two cultural values: collectivism and uncertainty avoidance. The theoretical and practical implications of applying theory of reasoned action and innovation diffusion theory to explain the adoption of new technologies in societies with different cultures are also discussed.National Research Foundation of Korea Grant funded by the Korean governmen

Presenting tiered recommendations in social activity streams

Modern social networking sites offer node-centralized streams that display recent updates from the other nodes in one's network. While such social activity streams are convenient features that help alleviate information overload, they can often become overwhelming themselves, especially high-throughput streams like Twitter’s home timelines. In these cases, recommender systems can help guide users toward the content they will find most important or interesting. However, current efforts to manipulate social activity streams involve hiding updates predicted to be less engaging or reordering them to place new or more engaging content first. These modifications can lead to decreased trust in the system and an inability to consume each update in its chronological context. Instead, I propose a three-tiered approach to displaying recommendations in social activity streams that hides nothing and preserves original context by highlighting updates predicted to be most important and de-emphasizing updates predicted to be least important. This presentation design allows users easily to consume different levels of recommended items chronologically, is able to persuade users to agree with its positive recommendations more than 25% more often than the baseline, and shows no significant loss of perceived accuracy or trust when compared with a filtered stream, possibly even performing better when extreme recommendation errors are intentionally introduced. Numerous directions for future research follow from this work that can shed light on how users react to different recommendation presentation designs and explain how study of an emphasis-based approach might help improve the state of the art

Multi-Dimensional-Personalization in mobile contexts

During the dot com era the word "personalisation” was a hot buzzword. With the fall of the dot com companies the topic has lost momentum. As the killer application for UMTS or the mobile internet has yet to be identified, the concept of Multi-Dimensional-Personalisation (MDP) could be a candidate. Using this approach, a recommendation of mobile advertisement or marketing (i.e., recommendations or notifications), online content, as well as offline events, can be offered to the user based on their known interests and current location. Instead of having to request or pull this information, the new service concept would proactively provide the information and services – with the consequence that the right information or service could therefore be offered at the right place, at the right time. The growing availability of "Location-based Services“ for mobile phones is a new target for the use of personalisation. "Location-based Services“ are information, for example, about restaurants, hotels or shopping malls with offers which are in close range / short distance to the user. The lack of acceptance for such services in the past is based on the fact that early implementations required the user to pull the information from the service provider. A more promising approach is to actively push information to the user. This information must be from interest to the user and has to reach the user at the right time and at the right place. This raises new requirements on personalisation which will go far beyond present requirements. It will reach out from personalisation based only on the interest of the user. Besides the interest, the enhanced personalisation has to cover the location and movement patterns, the usage and the past, present and future schedule of the user. This new personalisation paradigm has to protect the user’s privacy so that an approach supporting anonymous recommendations through an extended "Chinese Wall“ will be described