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The aim of this thesis is to study how students perceive and handle internet’s continuous flow of information in everyday life, through news report, social media and targeted advertising. By examining the students attitudes and thoughts about the extent to which they find the information they meet relevant this study seeks to understand how they find what is relevant to them and how they limit the intake of information by different combinations of selection processes. For this purpose interviews were carried out with seven university students from different fields and levels. Three categories of theories about selection processes serves as a theoretical framework for the analysis and results. The categories are referred to as external, personalized and personal factors. The external factors include gatekeeping theory, news valuation and agenda theory and the personalized factors include theories about filter bubbles and echo chambers. The personal factors include strategies to avoid information overload such as filtering and withdrawing, it also includes satisficing as a stop rule for information seeking. The study shows that the three categories of selection processes are affected by emotional factors referred to as simplicity, interest and sensation. These drives the student to get more deeply informed. The students attitudes towards personalized information shows to be divided between if it should be more firmly adapted to each individual, or if that would increase the feeling of being monitored. In the students reflections regarding saved information emerges three feelings of negativity described as reluctance, lack of control and unease. Despite these feelings of negativity the phenomenon is accepted. Seeing that the alternative is disconnecting from modern society and life, acceptance shows to be the most reasonable alternative.

The aim of this thesis is to study how students cope with crisis-related information, such as news reports and public discussions on the Syrian refugee crisis. With this in view, the study seeks to identify the strategies chosen to manage this sort of information, as well as the motivations underlying those choices. For this purpose inter-views are carried out with nine students of library and information science. Herbert Simon’s theory of bounded rationality serves as a theoretical framework for the analysis of the results, aided by more recent theories of the role of affect in decision making. The study shows that the students used various strategies for coping with crisis-related information. They used filtering and withdrawal as strategies for information avoidance, limiting the amount of information encoun-tered, and they used satisficing as a strategy for negotiating information needs, thus limiting their information seeking. The motivations for information avoidance were mainly emotion-based, while the motivations for satis-ficing were cognition-based. However, assuming all motivated, goal-oriented behaviour to be rational behaviour, the strategies used by the students were rational, or rather based on bounded rationality. All strategies also turned out to be flawed in some way, which is why a full repertoire of strategies – both emotion-based and cognition-based, with the ability to complement each other – is desirable. This is a two years master’s thesis in library and information science.

To United Minds, large-scale surveys are an important offering to clients, not least the public opinion poll Väljarbarometern. A risk associated with surveys is satisficing – sub-optimal response behaviour impairing the possibility of correctly describing the sampled population through its results. The purpose of this study is to – through the use of multivariate outlier detection methods - identify those observations assumed to be non-representative of the population. The possibility of categorizing responses generated through satisficing as outliers is investigated. With regards to the character of the Väljarbarometern dataset, three existing algorithms are adapted to detect these outliers. Also, a number of randomly generated observations are added to the data, by all algorithms correctly labelled as outliers. The resulting anomaly scores generated by each algorithm are compared, concluding the Otey algorithm as the most effective for the purpose, above all since it takes into account correlation between variables. A plausible cut-off value for outliers and separation between non-representative and representative outliers are discussed. The resulting recommendation is to handle observations labelled as outliers through respondent follow-up or if not possible, through downweighting, inversely proportional to the anomaly scores.