Abstract Combining computational technologies and humanities is an ongoing effort aimed at making resources such as texts, images, audio, video, and other artifacts digitally available, searchable, and analyzable. In recent years, deep neural networks (DNN) dominate the field of automatic text analysis and natural language processing (NLP), in some cases presenting a super‐human performance. DNNs are the state‐of‐the‐art machine learning algorithms solving many NLP tasks that are relevant for Digital Humanities (DH) research, such as spell checking, language detection, entity extraction, author detection, question answering, and other tasks. These supervised algorithms learn patterns from a large number of “right” and “wrong” examples and apply them to new examples. However, using DNNs for analyzing the text resources in DH research presents two main challenges: (un)availability of training data and a need for domain adaptation. This paper explores these challenges by analyzing multiple use‐cases of DH studies in recent literature and their possible solutions and lays out a practical decision model for DH experts for when and how to choose the appropriate deep learning approaches for their research. Moreover, in this paper, we aim to raise awareness of the benefits of utilizing deep learning models in the DH community.

Crowdsourcing platforms were initially designed to recruit people to perform tasks that were simple cognitively but difficult for computers. One challenge in these settings is to identify an incentive mechanism for motivating workers to complete tasks and do high-quality work. Previous research has studied the use of financial incentive mechanisms and social comparison as motivators. These mechanisms can only be applied to ground truth tasks, tasks for which there is an objective performance scale. In this paper, we define and compare three innovative methods for improving worker engagement on non-ground truth tasks drawing on a psychological theory of commitment. The three methods are similar in asking participants to promise they will complete a task, but they differ in terms of how the commitment is made. In the first method, participants commit by signing a contract; in the second, by listening to a recording; in the third, by recording a personal commitment. The last two methods significantly improved the task completion rate when compared to two baseline conditions. The methods we propose can be implemented simply, can be used for any task, and do not affect participants' behavior other than by improving their engagement.

This paper presents methods for improving the attention span of workers in tasks that heavily rely on their attention to the occurrence of rare events. The underlying idea in our approach is to dynamically augment the task with some dummy (artificial) events at different times throughout the task, rewarding the worker upon identifying and reporting them. The proposed approach is an alternative to the traditional approach of exclusively relying on rewarding the worker for successfully identifying the event of interest itself. We propose three methods for timing the dummy events throughout the task. Two of these methods are static and determine the timing of the dummy events at random or uniformly throughout the task. The third method is dynamic and uses the identification (or misidentification) of dummy events as a signal for the worker's attention to the task, adjusting the rate of dummy events generation accordingly.

This paper studies a new paradigm for improving the attention span of workers in tasks that heavily rely on user's attention to the occurrence of rare events. Such tasks are highly common, ranging from crime monitoring to controlling autonomous complex machines, and many of them are ideal for crowdsourcing. The underlying idea in our approach is to dynamically augment the task with some dummy (artificial) events at different times throughout the task, rewarding the worker upon identifying and reporting them. This, as an alternative to the traditional approach of exclusively relying on rewarding the worker for successfully identifying the event of interest itself. We propose three methods for timing the dummy events throughout the task. Two of these methods are static and determine the timing of the dummy events at random or uniformly throughout the task. The third method is dynamic and uses the identification (or misidentification) of dummy events as a signal for the worker's attention to the task, adjusting the rate of dummy events generation accordingly. We use extensive experimentation to compare the methods with the traditional approach of inducing attention through rewarding the identification of the event of interest and within the three. The analysis of the results indicates that with the use of dummy events a substantially more favorable tradeoff between the detection (of the event of interest) probability and the expected expense can be achieved, and that among the three proposed method the one that decides on dummy events on the fly is (by far) the best.

This paper represents a paradigm shift in what advice agents should provide people. Contrary to what was previously thought, we empirically show that agents that dispense optimal advice will not necessary facilitate the best improvement in people's strategies. Instead, we claim that agents should at times suboptimally advise. We provide results demonstrating the effectiveness of a suboptimal advising approach in extensive experiments in two canonical mixed agent-human advice-giving domains. Our proposed guideline for suboptimal advising is to rely on the level of intuitiveness of the optimal advice as a measure for how much the suboptimal advice presented to the user should drift from the optimal value.

Peer Designed Agents (PDAs), computer agents developed by non-experts, is an emerging technology, widely advocated in recent literature for the purpose of replacing people in simulations and investigating human behavior. Its main premise is that strategies programmed into these agents reliably reflect, to some extent, the behavior used by their programmers in real life. In this paper we show that PDA development has an important side effect that has not been addressed to date -- the process that merely attempts to capture one's strategy is also likely to affect the developer's strategy. The phenomenon is demonstrated experimentally, using several performance measures. This result has many implications concerning the appropriate design of PDA-based simulations, and the validity of using PDAs for studying individual decision making. Furthermore, we obtain that PDA development actually improved the developer's strategy according to all performance measures. Therefore, PDA development can be suggested as a means for improving people's problem solving skills.

In this paper we empirically investigate the feasibility of using peer-designed agents (PDAs) instead of people for the purpose of mechanism evaluation. This approach has been increasingly advocated in agent research in recent years, mainly due to it

The blooming of comparison shopping agents (CSAs) in recent years enables buyers in today's markets to query more than a single CSA while shopping, thus substantially expanding the list of sellers whose prices they obtain. From the individual CSA point of view, however, the multi-CSAs querying is definitely non-favorable as most of today's CSAs benefit depends on payments they receive from sellers upon transferring buyers to their websites (and making a purchase). The most straightforward way for the CSA to improve its competence is through spending more resources on getting more sellers' prices, potentially resulting in a more attractive ``best price''. In this paper we suggest a complementary approach that improves the attractiveness of the best price returned to the buyer without having to extend the CSAs' price database. This approach, which we term ``selective price disclosure'' relies on removing some of the prices known to the CSA from the list of results returned to the buyer. The advantage of this approach is in the ability to affect the buyer's beliefs regarding the probability of obtaining more attractive prices if querying additional CSAs. The paper presents two methods for choosing the subset of prices to be presented to a fully-rational buyer, attempting to overcome the computational complexity associated with evaluating all possible subsets. The effectiveness and efficiency of the methods are demonstrated using real data, collected from five CSAs for four products. Furthermore, since people are known to have an inherently bounded rationality, the two methods are also evaluated with human buyers, demonstrating that selective price-disclosing can be highly effective with people, however the subset of prices that needs to be used should be extracted in a different (and more simplistic) manner.