Modern Information Overload

Many solutions aimed to solve IO through time, researchers resorted to books and encyclopaedias and, later, data bank. Still, IO is worse than ever. So, where do sufferers go from this point? (Lock, 1982). So far, research in the domain of information Science (Borko, 1968), (Stock and Stock, 2013) indicates that although many available solutions were provided to solve IO, the flood is increasing and researchers are not able to control the problem.

Locks complains are still valid because information seekers are now in even more critical situations when seeking for information on the internet. IO has been a problem for few decades. Many people suffered, and many are still suffering. In the past decades, IO manifested itself in many ways, it ranged from simple issues of being overloaded with information, and also include serious problems such as inability to cope with information floods. Hence, information seekers always feel the anxiety of not having enough knowledge to fulfil certain information need.

Technology advancements in the 21st century have certainly been vast: being able to access information anytime on the go in an ad-hoc basis exceeds what Bush’s envisioned in his article “As We May Think” (Bush, 1945). Information seekers are now able to access any type of information instantly; thus, sharing information is even easier than imagined. Cutting edge technology changes in the blink of an eye, competition in technological aspects are rapid to the point that technology volumes and new releases are

35

made on a weekly basis which makes it hard if not impossible to keep up. All these advances are good, but increased the pressure on information seekers’ ability to process the incoming information through all these communication means and channels. Hence, information seekers suffer more of IO than ever.

This leads the discussion to new type of IO “Modern Information Overload (MIO)”. MIO eliminated the bounders between different types of IO; thus, MIO include all of them and adds them problems of information retrieval techniques on the internet. It further includes information (knowledge representation, education, healthcare, businesses and services) transformation into the internet.

Hence, researchers attempt to resolve this problem includes investigating the domain where IO happens, characterise causes, understand the behaviour of the effected group of information seekers and most importantly investigate the short comings of technology in use to either improve it or propose new solutions.

Bawden and Robinson attempts to identify potential factors of modern information communication and the role of pathologies of information in the quality and quantity of available information on the internet. Furthermore, the investigation also includes the impact of quantity and diversity on IO and information anxiety, and changing information in parallel with advent web 2.0 tool. Results indicate that these factors directly influence the quality of generated information though modern communication tools, thus, issues such as identity and authorship, novelty, and impermanence of information cannot be guaranteed (Bawden, 2009).

Williamson and Eaker, work describes the relationship between IO and psychometrics science, which studies and measures mental capacity and process of information seekers with regards to demographic data such as gender, age, and life satisfaction. The investigation examined information seekers (librarians, information science and psychology students) ability to manage information, mental states of feeling overwhelmed, focus and attention to process huge amount of information or learn new topics, decision making technique in the process of choosing topics of interest, the role of technology (email, fax, phone, messages) which creates floods of information, sheer volumes, continuous development in domain of expertise and search results that pressures information seekers ability of information processing (Williamson and Eaker, 2012).

36

Results indicated that information seekers tend to demand especially when they progress in academic settings. Most of the information-related demands are associated with increasingly complex assignments or job environment. Therefore, it is normal to experience higher levels of IO (Williamson and Eaker, 2012).

Technology brought a lot of benefits to information science. Hence, IT, IS, and information communication in parallel enhanced the way information can be searched and retrieved. The improved information communication techniques -which can be found in the domain of search engines, RSs web 2.0 tools and social media on the internet- further participated in speeding up the process of information creation, dissemination, retrieval and availability. Hence, information seekers live in an information centric age. An information explosion that exceeds information seekers ability to process. Therefore, Modern Information Overload (MIO) is associated with four important aspects of information: storing, sorting, selecting and summarizing. If existing tools and technologies are not able to provide information seekers with search results with regards to these aspects, then information seekers will be overloaded with un required information. Consequently, MIO occurs through abundance of accessible information that or not organized, filtered, or presented in an appropriate way to maximize access to accurate and relevant use of this information (Alexander et al., 2016). Therefore, researchers still show interest in exploring the phenomenon of IO. many studies indicate that IO affects information seeker productivity when learning and/or at work.

There are many approaches that address modern IO. The most influential are algorithms behind search engines such as Google (https://www.google.co.uk), Yahoo (http://uk.yahoo.com/), Bing (http://www.bing.com/), Semantic Search Engines (SSE) CarrotSearch (http://search.carrot2.org/stable/search), Clusty (http://clusty.com/), and Yippy (http://yippy.com/). They have various mechanisms and utilities embedded techniques in them, which generate search results according to user’s queries (i.e. keywords). It is difficult to find their search algorithms, because they are business secrets of these companies. However, there are numerous works which improved the performance of search engines through either new algorithms, or ranking and filtering of retrieved search results (Baeza-Yates, 2006) (Cortes et al., 2007) (Baeza-Yates et al., 2007) (Su et al., 2010). Approaches also include RSs found in e-commerce such as (Ullman, 2012) (Felfernig et al., 2007) (Adomavicius and Tuzhilin, 2005) and attempts

37

to improve RSs performance though CF, CBF and Hybrid filtering techniques (Zhao et al., 2011b) (Pazzani and Billsus, 2007) (Herlocker et al., 2004) (Burke, 2002).