Information Overwhelm Solutions & Treatments

Increase The Signal


There is an under-estimated cost of sifting through junk and noise to get to what’s valuable:

  • Distraction
  • Shifting emotions
  • Draining energy



  • Pay for premium content
  • Fractal reading

Social Media

  • Pay for Twitter blue
  • Search people’s top retweets
  • Turn off newsfeeds, trending, and recommendations


  • Gmail Priority Inbox
  • Scheduling
  • Mass unsubscribe

Simple Filters

Breakthrough Knowledge


Does this have the potential to fundamentally change me for the better?

When It’s Helpful

To avoid incremental, false learning that feels like learning, but isn’t really helping you.

Multiplier Skills Checklist


  • Future
  • Useful (breakthrough?)
  • Now
  • Rare
  • U
  • Curious
  • C

When It’s Helpful

To avoid

Hockey Stick Rule


If it’s trending for awhile, spend 5 hours exploring it.

When It’s Helpful

To avoid under- or over-exploring.

Energy Filter


Does this give me energy? Am I attracted to it?

Given the costs and nature of information overwhelm, a solution needs to accopmlish a few things:

  • Help you find, make sense of, make decisions with, and apply high-quality information with the least amount of effort
  • Help deal with the cumulative chronic stress of emotional pangs
  • Have an approach that is future-proof given the escalating nature of information overwhelm

Information Literacy

  • Planning
  • Attention to detail
  • Ability to find good sources
  • Effective search procedures
  • Managing environment
  • Ignore certain information
  • Taking action without having all the facts
  • Create an information queue
  • Filter information ruthlessly;
  • Delegate information responsibly
  • Learn to skim read.
  • Slow reading & reflection


A closely related viewpoint is that overload, assuming that it exists, is not really caused by TMI, since there is never a necessary for anyone to absorb all relevant information; rather it is caused by "filter failure", an inability, which may be due to a variety of causes, to identify from the mass of available information what is useful to us to any particular time; for a clear identification of this ides in the context of health information, see Klerings, Weinhandl and Thaler (2015). —Study: Information Overload - An Overview
Savolainen (2007), as noted earlier, identifies filtering as a valuable mechanism for reducing overload. He denoted a filtering strategy as a disciplined and systematic attempt to focus on relevant information from chosen sources, by specifying criteria for immediately removing items from consideration. These criteria will necessarily be different for each source, and may be applied intellectually or algorithmically. Manheim (2014), Shachaf, Aharony and Baruchson, (2016), Feng and Agosta (2017), Saxena and Lamest (2018) and Jones and Kelly (2018) also identify filtering as a major strategy for avoiding overload. The behavioral decision theory literature in essence also assumes that decision makers do not consider everything in making choices (Lau, 2019). —Study: Information Overload - An Overview

Types Of Filtering

It may involve a variety of processes for selecting, omitting, and ranking information (Belkin and Croft 1992, Rader and Grey 2015, Saxena and Lamest (2018). A distinction is sometimes made between active filtering, seeking useful information and drawing it to the user's attention, and passive filtering, omitting less useful material from that presented to the user. Filtering may be done automatically on the basis of explicitly asking for user preferences. Alternatively, it may be done algorithmically , by simple means, such as by noting what kinds of email messages are deleted unread, or by more complex means, using techniques such as machine learning; for examples of the latter, see Jones and Kelly (2018). It can be achieved by means of organizational procedures; elite politicians, for example, were noted to filter incoming information through procedures and the use of assistants as information intermediaries Walgrave and Dejaeghere (2017). —Study: Information Overload - An Overview

Costs Of Filtering

Filtering is always a trade-off. It helps reduce overload by allowing users to concentrate on useful information, but may cause them to miss serendipitous encounters with novel information, and may discourage exploration. There is also an ethical question about who, or what, is controlling what information a user sees. An antidote to this may be to ensure that filtering is always done transparently transparently (Jones and Kelly 2018, Raderand Grey 2015). —Study: Information Overload - An Overview

Examples Of Filtering

  • Ignoring emails and social media notifications from certain people and about certain topics
  • Unfollowing accounts on social media
  • Examining only the most recent, or the most relevant, items from a long list
  • Examining only items inlanguages in which one is fluent, rather than seeking a translation for others

Study: Information Overload - An Overview


Definition Of Satisficing

Satisficing, also termed bounded rationality, is a way of making decisions and choices when it not feasible to fully compare the benefits of possible options; in essence, a way of efficiently getting something that, while not necessarily optimal, is good enough for the purpose (Simon 1955, Gigerenzer and Selten 2001; Stevens, 2019). —Study: Information Overload - An Overview

Satisficing Information

In the information context, provided that there is a good rationale for the decisions made, this can be a good heuristic for getting good enough information without being overloaded. Indeed, such behaviour, often quite sophisticated and usually involving withdrawing and filtering approaches, is commonly observed; see, for example, Agusto (2002), Prabha et al. (2007), Mansouran and Ford (2007), Savolainen (2007), Warwick et al. (2009), MacDonald, Bath and Booth (2011), Manheim (2014), and Shachaf, Aharony and Baruchson, (2016). It is sometimes clearly the predominant means of avoiding overload, as with the Belgian politicians studied by Walgrave and Dejaeghere (2017). It is often suggested that satisficing is an expression of Zipf's Principle of Least Effort, but Mannheim produces examples to show that this may not always be so; people do not always follow, in information terms, the path of least effort. —Study: Information Overload - An Overview

Good vs Bad Satisficing

Bawden and Robinson (2009) distinguish good satisficing from bad satisficing. Good satisficing requires a clear (to its user) rationale for why decisions are being taken. Bad satisficing reduces to an essentially random and contingent selection of sources and material, and to an avoidance of information. The former is a good solution to perceived overload; the latter, while it may easy anxiety, is unlikely to be effective where the information carries any real significance for its user, life, work or study. Cooke (2017) points to the danger of bad satisficing in relation to problems of post-truth and alternative facts, and in particular to the spreading of fake news.

Avoiding And Withdrawing

The rather crude heuristic of information avoidance relies on simply ignoring potentially useful information, and sources of information, either because there is just too much to deal with, or because it is incongruent, difficult to fit with the user's existing knowledge (Sweeny et al. 2010, Neben 2015)... As Johnson (2014), and the sources which he quotes, point out, avoidance, or escape, may be a perfectly rational response to overload, if one cannot make any use of the information obtained. Manheim (2014), somewhat similarly, argues, that not seeking for information may be a perfectly reasonably course of action in some circumstances, and will certainly prevent, or at least minimize, overload. However, more negatively, avoidance may lead to avoiding disquieting or discordant information, which can lead to escaping, seeking simple solutions to complex issues by avoiding information which may be challenging or unsettling, or even by turning to demagogues (Johnson 2014). Case and Given (2016 pp.115-116) use selective exposure for much the same strategy. A more nuanced approach, identified by Savolainen (2007) is information withdrawal, a conscious decision to keep to a minimum the number of sources to be considered, ideally combined with a filtering of intake, and a rapid weeding of relevant material of limited usefulness. This strategy has been noted by other researchers; see, for example, Shachaf, Aharony and Baruchson, (2016), Sasaki, Kawai and Kitamura (2016), Liang and Fu (2017), Feng and Agosta (2017), and Saxena and Lamest (2018). The senior politicians studied by Walgrave and Dejaeghere (2017) placed much reliance on this approach, focusing on information matching their ideology (party leaders) or their specialist brief (ministers). Examples of withdrawal are: * customising social media to limit the number of notification received * unfriending or unfollowing social media accounts * turning off mobile devices, or ignoring email or social media, for a period * focusing solely on information matching existing knowledge or frame of reference * leaving a social media platform entirely. —Study: Information Overload - An Overview
Info-anxious students often sacrifice their information seeking (by ending their research with minimal or poor resources) or sometimes abandon it altogether (Blundell & Lambert, 2014, p. 263). Study: A Generation of Information Anxiety: Refinements and Recommendations


Solutions To Overwhelm

  • Build up a base of knowledge like a top performer does so new information feels less complex.
  • Turn data into wisdom
    • Information architecture is the way that we arrange the parts of something to make it understandable as a whole.
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  • Certainly more reliance is placed by academic and professional readers on abstracts and summaries, as opposed to a reading of the full document; see, for example, Nicholas, Huntington and Jamali (2007). Whilst a reasonable, and long-standing, way of coping of an excessive number of potentially useful things to be read, this is potentially troubling, as studies have shown that typically 20% of abstracts contain significant inaccuracies (see, for example, Hartley and Betts 2009); usually presenting the subject matter of the main document in an unreasonably positive light. the same must surely be true of policy makers and administrators.