Evolution of Ego-networks in Social Media with Link Recommendations

Ego-networks are fundamental structures in social graphs, yet the process of their evolution is still widely unexplored. In an online context, a key question is how link recommender systems may skew the growth of these networks, possibly restraining diversity. To shed light on this matter, we analyze the complete temporal evolution of 170M ego-networks extracted from Flickr and Tumblr, comparing links that are created spontaneously with those that have been algorithmically recommended. We find that the evolution of ego-networks is bursty, community-driven, and characterized by subsequent phases of explosive diameter increase, slight shrinking, and stabilization. Recommendations favor popular and well-connected nodes, limiting the diameter expansion. With a matching experiment aimed at detecting causal relationships from observational data, we find that the bias introduced by the recommendations fosters global diversity in the process of neighbor selection. Last, with two link prediction experiments, we show how insights from our analysis can be used to improve the effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl

Genres of search: A concept for understanding successive search behaviour

The paper presents Genres of Search, a concept that contributes to our understanding of the successive search phenomenon. The concept is explained in the context of a case study that used naturalistic methods to explore the information-seeking behaviour of 10 participants, aged 16 to 18, as they searched for, selected, and used information for a school-based inquiry project on a topic related to the history of Western civilization. The study found an array of sub-searches, or Genres of Search, embedded within the information problem solving process, each genre representing a distinct information need. The Genres of Search concept is useful for mapping irregularities in successive searching and provides insight into the nature of the tasks involved in the search process

Factors that Influence Information-Seeking Behavior : The Case of Greek Graduate Students

The purpose of this survey is to determine the information-seeking behavior of graduate students of the Faculties of Philosophy (8 Schools) and Engineering (8 Schools) at the Aristotle University of Thessaloniki. Discipline did not seem to affect information-seeking behavior critically. The Majority of the sample demonstrated a low to Medium level of information-seeking behavior. This survey revealed the need for improving the level of graduate students' information literacy skills

Categorising Search Sessions: Some Insights from Human Judgments

The session is a common unit of interaction that is used in search log analysis. By analysing sessions, it is possible to identify distinct classes of searcher behaviour that can be used to design search applications that better support groups of users based on their expected behaviours. This paper describes an online card sort experiment to investigate how people distinguish between search sessions (i.e., how they group them) to gain insights into their organising principles and to inform the future use of automated approaches, such as clustering. Results show patterns of user behaviour to be the most common way of grouping sessions

Mining search logs for usage patterns

One of the greatest opportunities and challenges of the 21st century is the ever increasing significance of data. Data underpins our businesses and our economy, providing awareness and insight into in every sphere of life; from politics to the environment, arts and society. The everyday interactions between people and devices can be harnessed to power a new generation of products and services, allowing us to better understand human needs, aspirations and behaviour. Of all the data to which we have access, there is none more valuable than the trace people leave when they search for digital information. In browsing the web, people reveal something about their behaviour and habits, but little about their intent. By contrast, when people search for information, they express in their own words their explicit needs and goals. This data represents a unique resource that offers extraordinary potential for delivering insights that can drive the next generation of digital services and applications. Various studies have been undertaken to understand how and why people interact with search engines. Such studies have led to the creation of frameworks that describe distinct patterns of use, ranging from individual queries to entire information seeking episodes. These patterns may focus on information seeking behavior [9], the types of search tasks that users perform [10], their goals and missions [5], their task switching behavior [4], or the tasks, needs and goals that they are trying to address when using search systems [10, 11]. Moreover, the academic community is not alone in showing an interest in mining search logs. Two highly influential commercial organisations, ElasticSearch and LucidWorks, have both recently released independent logfile analysis platforms (Kibana and SiLK respectively [13, 14]). What unites all of these efforts is the belief that finding distinct, repeatable patterns of behaviour can lead to a better understanding of user needs and ultimately a more effective search experience. In this chapter, we explore the use of data mining techniques to find patterns in search logs, focusing on the application of open source tools and publicly available data

User behaviour and task characteristics: A field study of daily information behaviour

Previous studies investigating task based search often take the form of lab studies or large scale log analysis. In lab studies, users typically perform a designed task under a controlled environment, which may not reflect their natural behaviour. While log analysis allows the observation of users' natural search behaviour, often strong assumptions need to be made in order to associate the unobserved underlying user tasks with log signals. We describe a field study during which we log participants' daily search and browsing activities for 5 days, and users are asked to self-annotate their search logs with the tasks they conducted as well as to describe the task characteristics according to a conceptual task classification scheme. This provides u