Category: haunting

Exploring Nursing Ghost Stories through Machine Learning: Topic…

Exploring Nursing Ghost Stories through Machine Learning: Topic Discovery with Latent Dirichlet Allocation

NOTE: Click to open graphics for an expanded and clearer view of the findings they contain  

As reported in earlier posts, the Allnurses.com web site hosts a long-running moderated discussion thread called “Nursing Ghost Stories” (NGS).  The NGS collection spans over a decade (2005-2017) amounting to 199 pages as of the time of this writing.  As a dataset NGS contains multiple first and second hand accounts and commentary on paranormal type experiences 

The archive contains classic examples of hauntings and poltergeist phenomena.  Patients were generally the percipients in ghost experiences.   Sometimes the ghosts in question appeared to be former nurses in period dress, or former doctors and patients, or former area residents.  However, these kinds of paranormal experiences did not dominate the collection  

In actuality, the NGS archive conveys several varieties of psi and post-mortem survival phenomena.  The archive contains several examples of extrasensory perception and presentiment in particular  

There were also examples of after-death communication (ADC), which are sensed-presence or apparitional experiences involving deceased family members or friends.  Unlike hauntings which are place-centered, ADC encounters are person-centered involving meaningful coincidences (or synchronicities) for the percipients

The archive contains several reports of near-death experiences (NDEs). However, the more representative encounters involved nearing death awareness (NDA) type experiences.  In NDA situations, terminally-ill patients experiencing death-bed visions will have perceptions of welcoming apparitions of deceased relatives or loved ones

  • Terminal patients will also appear to hold conversations with persons who are not physically present in their room.  Sometimes nurses described these aspects of NDA experiences as dementia
  • It is also not uncommon for gravely-ill patients to be alert and conversant in their final hours before death, a phenomenon called “terminal lucidity”

Provided below are examples of exchanges regarding NDA situations as characterized by nurses working in long-term care and palliative care settings 

I’ve been a hospice nurse for 5 years. I have been with hundreds of people at the time of their death & I can tell you first hand that if the patient is alert enough to speak, you’ll hear them talking to loved ones that have already passed over

That is so true. I, too am a hospice nurse and when pts. start talking to their dead relatives, you know that they have about a week MAX before they are gone

From experience I’ve learned that when a pt tells you they’re going to die…they usually do…and if they start talking to dead family members…they usually die…it’s like the family members have come to take them…..

As a follow-on to the earlier wordcloud project, we wondered whether unsupervised machine learning, specifically topic generation models, could discover the abovementioned themes in the NGS archive 

  • Generative topic models

    view documents as having a latent semantic structure of topics that can be inferred from co-occurrences of words in documents  

  • For this project, the Latent Dirichlet Allocation (LDA) topic model was employed.  LDA views documents as probability distributions over topics and topics as probability distributions over words
  • All documents share the same collection of topics, but each document contains those topics in different proportions.  The LDA algorithm samples words across topics until it arrives at topics and word selections that most likely generated the documents

Various packages and libraries for natural language processing within Python were used to include: the Natural Language ToolKit (NLTK) for processing the data set; scikit-learn to prepare and fit the LDA model; pyLDAvis to display the results and t-Distributed Stochastic Neighbor Embedding (t-SNE) to map topic distances

The project pipeline involved: data set processing; conversion of words and documents into a document-term matrix and vector space; fitting the LDA models; and displaying the results

Processing. The data set was decomposed into 199 documents from its constituent web pages.  In contrast to the wordcloud project, the set of stopwords was enlarged to find meaningful insights in the NGS archive

  • The core set of stopwords consisted of commonly-used prepositions, conjunctions, and contractions.  Stopwords from the wordcloud application were used as a start point for this purpose
  • Since the archive consisted of first or second hand accounts, words related to stories and/or storytelling were added to stopwords, along with words related to the maintenance of the thread
  • Since spontaneous experiences can occur at any moment, words conveying times were removed.  While many experiences were singular events, numeric references involving ordinal (e.g. one, two) and cardinal (e.g. first, second) rankings were removed
  • Titles of persons were removed (e.g. Mr., Mrs., etc.); however, person and gender types (e.g. man, woman, etc.) and interpersonal relationships (e.g. family, friends, or strangers) were preserved
  • Domain-related words relating to patient care or standard procedures were removed (e.g. hospital, unit, shift, staff, work, station, monitor, code)

Conversion. Vector transformations converted the data set into a document-term matrix for mathematical processing.  The rows of the matrix correspond to documents with columns corresponding to the frequency of a term

  • Count vectorizers count word frequencies.  Term Frequency-Inverse Document Frequency (TF-IDF) vectorizers normalize (divide) word counts by their frequency in the documents
  • Both vectorizers converted words to lower case and removed non-word expressions. The vectorizers were parameterized to look for bigrams (or words that were often used together) 

Model Fit/Display. The LDA model was fitted using ten topics.  Words within topics were sorted and ranked with respect to their frequency in and relevance within a topic

  • The LDA model was fitted with using Count and TF-IDF vectorization and ran with a maximum of 100 iterations.  LDA model results were displayed using pyLDAvis and t-SNE to map topic distances

Results. Although topics produced from the model are unlabeled, words within topics usually can be woven into a coherent theme

The first four pyLDAvis graphs provide the top 30 words and bigrams in Topics 1 through 4 using Count vectorization  

  • Topic 1 is the most representative of the body of stories in the thread and generated around 86% of the content.  Words in Topic 1 included: “nurse” and “patient”; both nurses and patients were percipients and sometimes sources of “ghost” experiences.  If apparitions represented unrecognized persons, patients had “asked” whom they “saw.”  Many apparitional encounters involved patients who were “heard” “talking” to deceased “family” members or a “friend.“  These telepathic types of apparitions were often described as “sitting” near the bedsides of patients, or transiting their rooms or into an adjacent “hall” on their “floor.”  Overall, this could be considered an apparitional experiences topic

  • Topic 2 is derived from user commentary and seems reflective of internal varieties of psi functioning. Words in Topic 2 included:  “dreams”, “feel(ings)” and a “sense” of awareness or presentiment of events that were happening or about to “happen”, usually in connection with the deaths of family members. In other cases the dreams were possible telepathic connections with lost “loved” ones. Overall, this can be considered a extrasensory perception topic and it generated 7% of the content  
  • Topic 3 appears reflective of external forms of psi and survival phenomena to include auditory and physical encounters commonly associated with hauntings and poltergeists.  Words in Topic 3 included: “haunted”, “voice(s)”, and other imitative sounds such as “music.”  There were also reported instances of anomalous telephone contact possibly involving “phone” calls from the dead and “strange” behaviors of televisions, call lights and other electrical appliances.  Overall, this could be considered a hauntings and poltergeists topic and it generated around 4% of the content 
  • Topic 4 is also derived from user commentary and seems reflective of general discussions on the paranormal, religious and exceptional experiences.  Discussions included: “paranormal” television, “movie” and “radio” entertainment;  synchronicities (meaningful coincidences) and “photo” and other evidence from paranormal investigations.  Discussions also involved ghost stories outside a nursing context; some were urban legends and a few were probably larks.  Overall, this could be considered a paranormal discussions topic and it generated around 3% of the content

The fifth pyLDAvis graph provides the top 30 words in Topic 1 using TF-IDF vectorization.  

  • The findings were close to those encountered for Topic 1 with the Count Vectorization.  However, it appears to be a combined apparitional experiences and extrasensory perception topic accounting for 94% of the content.

     This consolidation arises from the fact that TF-IDF vectorization lowers the contribution weight of commonly used words

This project again demonstrates the usefulness of topic generation models for finding meaningful patterns in masses of unlabeled or unstructured data.

  The LDA topic discovery method indicated several varieties of psi and survival experiences that went beyond ghost stories 

  • Many apparitional encounters described in the archive represented the intersection of nearing death awareness (involving death-bed visions of welcoming apparitions) and after-death communication experiences (involving apparitions of deceased family members and friends)
  • Even though the algorithm knows nothing intrinsically about the above experiences, the model was able to infer topics and words corresponding to the most representative kinds of encounters 

Greater insights could be gained by structuring the NGS dataset and labeling the experiential elements within it.  Follow-on research could employ semi-supervised methods to train models to classify types of psi and survival experiences and to find correlates within them  

Specifically, deep learning models could be trained on the semantics around typologies of apparitions with tagged documents.  Parapsychology categorizes apparitions along four lines: living agent; crisis; post-mortem; and haunting  

  • If an apparition is seen within ±12 hours of a person’s death, that represents a crisis apparition 
  • If an apparition is seen 24 hours or more after a person’s death, that apparition is post-mortem
  • If the apparition is of a long-deceased person and has a location affinity, that is a haunting apparition

Nonetheless, the apparitional experiences in NGS appear roughly consistent with survey results elsewhere.  Apparitional experiences rarely occur in the general population, but when they do, the apparitions are likely to represent recognized persons, known to the individuals who are perceiving them

REFERENCES

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, (Jan), 993-1022.

Gauld, A., & Cornell, A. D. (1979). Poltergeists. Routledge Kegan & Paul.

Kircher, P. and Callanan, M. (2017, Dec 14).  NDEs and Nearing Death Awareness in the Terminally Ill. International Association for Near Death Studies (IANDS).

Natural Language Toolkit: NLTK 3.2.5 documentation. (2017, Sep 24). NLTK Project.

Pearson, P. (2014). Opening Heaven’s Door: What the Dying May be Trying to Tell Us about where They’re Going. Random House Canada. Sponsored

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … & Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830.

Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. In Proceedings of the workshop on interactive language learning, visualization, and interfaces (pp. 63-70).

What’s Your Best Nursing Ghost Story? (2017, Oct 30). AllNurses.com

IMAGES

pyLDAvis Graph of Topic 1 (Count Vectorization) from Nursing Ghost Stories Corpus. (2018, Apr 08). © Maryland Paranormal Research ®.  All rights reserved.

pyLDAvis Graph of Topic 2 (Count Vectorization) from Nursing Ghost Stories Corpus. (2018, Apr 08). © Maryland Paranormal Research ®.  All rights reserved.

pyLDAvis Graph of Topic 3 (Count Vectorization) from Nursing Ghost Stories Corpus. (2018, Apr 08). © Maryland Paranormal Research ®.  All rights reserved.

pyLDAvis Graph of Topic 4 (Count Vectorization) from Nursing Ghost Stories Corpus. (2018, Apr 08). © Maryland Paranormal Research ®.  All rights reserved.

pyLDAvis Graph of Topic 1 (TF-IDF Vectorization) from Nursing Ghost Stories Corpus. (2018, Apr 08). © Maryland Paranormal Research ®.  All rights reserved.

Nursing Ghost Stories as told through Natural Language…

Nursing Ghost Stories as told through Natural Language Processing

Language comes natural and easy to humans but is difficult for machines because of its structure and ambiguity.  Ghost stories perhaps showcase that ambiguity as paranormal experiences are difficult to reconcile or explain within known scientific principles or inferences

Natural Language Processing (NLP) is a field of computing that enables computers to analyze, understand and communicate human language.  Today natural language processing powers several technologies such as: speech recognition assistants; language translation; sentiment analysis; entity and relationship recognition; as well as text summation, parsing and analysis

The Natural Language Toolkit (NLTK) is a platform of libraries and programs for natural language processing written in the Python programming language. NLTK was developed at the University of Pennsylvania and first released in 2001  

Word clouds are visual representations of a text, where the sizing of words displayed reflects their prominence or emphasis within the text.  The word cloud application used here was developed with NLTK and other Python modules

Word clouds provide high-level analysis of themes associated with a corpora (body) of text. In business, they can be used to highlight pain points from customer feedback.  For this effort, word clouds were applied to a collection of ghost experiences as told by nurses

Allnurses.com hosts a long-running discussion thread called “Nursing Ghost Stories” (NGS).  The NGS collection spans over a decade (2005-2017) amounting to 199 pages as of the time of this writing.  The NGS archive contains a mixture of first and second hand accounts along with commentary

Two corpora were developed from the NGS collection.  One corpus contained plain text and another corpus was tokenized (tagged) by sentences, words and parts of speech.  A word cloud was subsequently generated from the plain text corpus that is displayed above.  Common stop words, for example prepositions were  filtered out prior to the generation of the display

The word cloud is interesting for what it does and does not emphasize.  For example, the words ghosts and hauntings along with their roots or extensions are not prominent in the display.  Hauntings involve recurrent paranormal experiences commonly experienced in the form of ‘imitative noises” and in more elevated forms through apparitions. Many NGS discussions appear to involve sensed presence experiences

There also appears to be substantive emphasis on nearing death awareness (NDA) type experiences as said or told by patients, many of whom were in long-term care or hospice settings, to nurses under their supervision.  In their final phases, terminally-ill patients often perceive welcoming apparitions or visitations from deceased relatives or loved ones.  In NDA experiences, patients often appear to hold conversations with persons who are not physically present

As would be expected, terms of reference associated with medical profession are most prominent in the word cloud, however these terms could be filtered from future word clouds to potentially obtain deeper insights on the experiences

In the near-term, NGS corpora can be used to develop sentiment analysis. Also worth exploring are word pairings and conditional frequencies connected with them. A longer term effort would mix natural language processing with machine learning to characterize types of encounters within the NGS collection

REFERENCES:

Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: analyzing text with the natural language toolkit. “ O’Reilly Media, Inc.”.

Kircher, P. and Callanan, M. (2017, Dec 14).  NDEs and Nearing Death Awareness in the Terminally Ill. International Studies for Near Death Studies (IANDS).

Natural Language Toolkit: NLTK 3.2.5 documentation. (2017, Sep 24). NLTK Project

Pearson, P. (2014). Opening Heaven’s Door: What the Dying May be Trying to Tell Us about where They’re Going. Random House Canada. Sponsored

What’s Your Best Nursing Ghost Story? (2017, Oct 30). AllNurses.com

IMAGE

Wordcloud from NGS Corpus. (2018, Feb 19). © Maryland Paranormal Research ®.  All rights reserved.

they-hide-in-the-dark: O’Hare Mansion – 

they-hide-in-the-dark:

O’Hare Mansion – 

The O’Hare Mansion once stood in Greencastle, Indiana. It has since been demolished but has left behind a legacy of some of the best pieces of paranormal photography every captured. 

A man called Guy Winters was convinced by some friends to join them in exploring the ruins of old mansion. The building, which had been built by the O’Hare family in the 1800s, had been out of use and had fallen into disrepair. During his exploration of the house Guy took many photos of the building and when he had those pictures developed he was shocked to see ghostly apparitions in the finished photographs. Upon further examination he also found that the spirits appeared on the orginal negatives for the images. 

These ghosts have been nicknamed the Pink and Gold Ladies. Guy sent the photos to a tv station who tacked down the surviving members of the O’Hare family. He met with Mary O’Hare who recognised the Pink Lady in the photos as her mother, Irene O’Hare, and told him that the room the Pink Lady is standing in was once her mother’s bedroom. 

Is the Grinning Man real?

The Grinning Man was apparently first

encountered

in 1966, according to John Keel, the paranormal investigator mostly know for his book

The Mothman Prophecies. 

The story about the Ginning man involved 2 boys walking along a street in New Jersey in October 1966. The boys were walking in the same area that earlier that day a woman had reported being chased by a “tall, green man”, and the two were nervous about the story. They were to report later that they saw perhaps the same man, standing behind a fence in some brush, looking at a house across the road from the boys.

The being was behind them, and was wearing a green one-peice suit that reflected the streetlights nearby. It noticed the boys looking at him, turned and began to smile – a grin that stretched from ear to ear. Having elongated eyes, the boys could not make out whether the creature had ears, a nose or hair.

That same night, miles away, reports of a “bright-white” UFO began filtering in to the police, and several officers reportedly saw the object themsleves hovering near a local reservior.

source: http://www.theparanormalguide.com/blog/grinning-man

Poltergeist at School?

btfd-ghosts-legends:

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Paranormal investigator claims looking at ‘possessed doll’ makes you ill

Paranormal investigator claims looking at ‘possessed doll’ makes you ill

The best spot to go ghost hunting in Maryland

The best spot to go ghost hunting in Maryland:

Maryland Paranormal Research ® Ghost Expedition at Brunswick Heritage Museum filmed by WUSA9 (Andi Hauser and Dave Scarnato) on Oct 13 2017

Happy Halloween Everyone!Keep it spooky!

Happy Halloween Everyone!

Keep it spooky!

Dundalk Historic District Ghost Expedition 2017/Baltimore County…

Dundalk Historic District Ghost Expedition 2017/Baltimore County (“You All Look Tired”)

http://maryland-paranormal.com DRV stream (“You All Look Tired”) captured by Maryland Paranormal Research ® at the Dundalk Patapsco Neck Historical Society and Museum [Dundalk MD] Sep 30 – Oct 1 2017. The investigation was part of the National Ghost Hunting Day 2017 event. Communication streams appear to acknowledge difficulties the investigator was experiencing in hearing real-time responses due in part to fatigue (“You All Look Tired”) but primarily due to the noise floor.  Some streams also appeared to indicate a presence had entered the room. Audio was captured with a SONY DCR SR45 Handycam, MACKIE 402-VLZ3 Mixer, HARMON DIGITECH 1066 Vocal Processor, ART EQ-351 31 Band 1/3 Octave Graphic Equalizer, and TIMEWAVE DSP-599zx Digital Noise Filter. The audio was further enhanced with noise filtering and limiting using AUDACITY. [HEADPHONES RECOMMENDED]

Most Spooky Castle (2017): Chillingham Castle, EnglandThis years…

Most Spooky Castle (2017): Chillingham Castle, England

This years winner of the most spooky castle award goes to
Chillingham Castle in England. Chillingham Castle is one of the worlds most
haunted castles known for its many ghosts and rare cows (not ghost cows for
anyone wondering). It was originally built in the 12th century as a
monastery. Eventually it became a royal castle in 1344 where it occupied a
strategic position during Northumberland’s bloody border feuds. Visitors to the
castle included King Henry III, King Edward I, James I, Charles I, and the
infamous Henry VIII. During WWII the castle was used as a barracks until a
baronet purchased the estate and restored it to its former glory. Today the
castle is surrounded by a park which is the home to about 90 rare wild cows.
Other than being known for its cows, the castle is well known for its ghosts.

One of the most famous ghosts in the castle is that of the
“Radiant Boy.” This spirit is a young child who can be seen floating around the
Pink Room. His cries can be heard echoing throughout the hallways at the stroke
of midnight. For a while the cries seemed to come from a spot where there is a
passage cut into the wall into the adjoining tower. As the cries disappeared a
white light would appear and the figure of a young boy dressed in blue would
approach those within the room. While the room was undergoing renovations, the
bones of a young child, surrounded by fragments of blue cloth, were found
within the walls. The bones were removed and given a proper burial. For a while
the Blue Boy was seen no longer. However once the room was rented out again to
weary travelers, reports of a blue flash shooting out of the wall began to
surface.

Another ghost seen within the castle is the White Pantry
Ghost. At one point in time the Castles silver was stored in what was known as
the The Inner Pantry. One night while a guard was guarding the silver he was
approached by a very pale woman in white begging him for water. Thinking it was
a castle guest, the guard turned to get the water. At this point he remembered
that he was locked in the room and that no on else could have possibly entered.
This same figure is still seen begging for water today. It is thought that, due
to the nature of her begging for water, she had died from poisoning.

The ghost of the former Lady Berkeley can be heard walking
through the castle corridors. In life Lady Berkeley was the wife of Lord Grey.
However, Lord Grey ran off with her own sister, Lady Henrietta. As a result
Lady Berkeley was left abandoned in the castle. Today visitors can hear the
rustle of her dress as she walks along. Visitors also note a cold chill when
she goes past.

There are many other spirits to haunt the castles. Those who
stay overnight in the chambers have reported feeling uneasy and as if something
were watching them. In the Chapel the voices of two men talking are often
heard. No one knows what they are saying as the words are impossible to make
out and they stop talking if one tries to seriously listen. Also, at night in
the Courtyard people have reported seeing figures and walking shadows.

(sources: http://www.chillingham-castle.com/GhostsPg.asp?S=3&V=1&P=34,  http://www.atlasobscura.com/places/chillingham-castle,
https://great-castles.com/chillinghamghost.php)