Voting Advice Applications (VAAs) are Web tools that are used to inform increasing numbers of voters during elections. This increasing usage indicates that VAAs fulfill voters' needs, but what these needs are is unknown. Previous research has shown that such tools are primarily used by young males and highly educated citizens. This suggests that VAAs are generally used by citizens who are already well-informed about politics and may not need the assistance of a VAA to make voting decisions. To analyze the functions that VAAs have for their users, this study utilizes unique user data from a popular Dutch VAA to identify different user types according to their cognitive characteristics and motivations. A latent class analysis (LCA) resulted in three distinct user types that vary in efficacy, vote certainty, and interest: doubters, checkers, and seekers. Each group uses the VAA for different reasons at different points in time. Seekers' use of VAAs increases as Election Day approaches; less efficacious and less certain voters are more likely to use the tool to become informed.

Survey designers have long assumed that respondents who disagree with a negative question ("This policy is bad.": Yes or No; 2-point scale) will agree with an equivalent positive question ("This policy is good.": Yes or No; 2-point scale). However, experimental evidence has proven otherwise: Respondents are more likely to disagree with negative questions than to agree with positive ones. To explain these response effects for contrastive questions, the cognitive processes underlying question answering were examined. Using eye tracking, the authors show that the first reading of the question and the answers takes the same amount of time for contrastive questions. This suggests that the wording effect does not arise in the cognitive stages of question comprehension and attitude retrieval. Rereading a question and its answering options also takes the same amount of time, but happens more often for negative questions. This effect is likely to indicate a mapping difference: Fitting an opinion to the response options is more difficult for negative questions.

In this paper, we focus on data-driven approaches to human activity recognition (HAR). Data-driven approaches rely on good quality data during training, however, a shortage of high quality, large-scale, and accurately annotated HAR datasets exists for recognizing activities of daily living (ADLs) within smart environments. The contributions of this paper involve improving the quality of an openly available HAR dataset for the purpose of data-driven HAR and proposing a new ensemble of neural networks as a data-driven HAR classifier. Specifically, we propose a homogeneous ensemble neural network approach for the purpose of recognizing activities of daily living within a smart home setting. Four base models were generated and integrated using a support function fusion method which involved computing an output decision score for each base classifier. The contribution of this work also involved exploring several approaches to resolving conflicts between the base models. Experimental results demonstrated that distributing data at a class level greatly reduces the number of conflicts that occur between the base models, leading to an increased performance prior to the application of conflict resolution techniques. Overall, the best HAR performance of 80.39% was achieved through distributing data at a class level in conjunction with a conflict resolution approach, which involved calculating the difference between the highest and second highest predictions per conflicting model and awarding the final decision to the model with the highest differential value.

In many countries with multiparty systems, a decline in class voting has increased volatility and the need for comprehensive information about the political landscape among voters. Voting Advice Applications (VAAs) are online tools that match users to political parties and, as such, they hold a promise of reinforcing informational transparency and democratic representation. The current research investigated whether VAAs live up to this expectation by investigating to what extent VAAs affected users' political knowledge and vote choice in the Dutch national elections of 2012. Results show that VAA users feel that the VAA improved their political knowledge. In addition, those groups of VAA users who experienced a large knowledge increase, also relatively often indicated that their vote choice had been affected. This suggests that VAAs contribute to informational transparency by increasing knowledge among a potentially wide audience, and also that VAAs might increase democratic representation to the extent that VAAs persuade people to vote for the candidate that best represents their opinions. On the other hand, we found discrepancies between behavioural and perceptual measurements of the effect of VAAs on vote choice. This raises doubts about whether VAAs shape actual voting behaviours and knowledge, or rather perceptions of that.