Facebook status update messages
Facebook status update messages

Prompt:
You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Article: Introduction
Explanations of happenings in one's life, causal explanations, are an important topic of study in social, psychological, economic, and behavioral sciences. For example, psychologists have analyzed people's causal explanatory style BIBREF0 and found strong negative relationships with depression, passivity, and hostility, as well as positive relationships with life satisfaction, quality of life, and length of life BIBREF1 , BIBREF2 , BIBREF0 .
To help understand the significance of causal explanations, consider how they are applied to measuring optimism (and its converse, pessimism) BIBREF0 . For example, in “My parser failed because I always have bugs.”, the emphasized text span is considered a causal explanation which indicates pessimistic personality – a negative event where the author believes the cause is pervasive. However, in “My parser failed because I barely worked on the code.”, the explanation would be considered a signal of optimistic personality – a negative event for which the cause is believed to be short-lived.
Language-based models which can detect causal explanations from everyday social media language can be used for more than automating optimism detection. Language-based assessments would enable other large-scale downstream tasks: tracking prevailing causal beliefs (e.g., about climate change or autism), better extracting process knowledge from non-fiction (e.g., gravity causes objects to move toward one another), or detecting attribution of blame or praise in product or service reviews (“I loved this restaurant because the fish was cooked to perfection”).
In this paper, we introduce causal explanation analysis and its subtasks of detecting the presence of causality (causality prediction) and identifying explanatory phrases (causal explanation identification). There are many challenges to achieving these task. First, the ungrammatical texts in social media incur poor syntactic parsing results which drastically affect the performance of discourse relation parsing pipelines . Many causal relations are implicit and do not contain any discourse markers (e.g., `because'). Further, Explicit causal relations are also more difficult in social media due to the abundance of abbreviations and variations of discourse connectives (e.g., `cuz' and `bcuz').
Prevailing approaches for social media analyses, utilizing traditional linear models or bag of words models (e.g., SVM trained with n-gram, part-of-speech (POS) tags, or lexicon-based features) alone do not seem appropriate for this task since they simply cannot segment the text into meaningful discourse units or discourse arguments such as clauses or sentences rather than random consecutive token sequences or specific word tokens. Even when the discourse units are clear, parsers may still fail to accurately identify discourse relations since the content of social media is quite different than that of newswire which is typically used for discourse parsing.
In order to overcome these difficulties of discourse relation parsing in social media, we simplify and minimize the use of syntactic parsing results and capture relations between discourse arguments, and investigate the use of a recursive neural network model (RNN). Recent work has shown that RNNs are effective for utilizing discourse structures for their downstream tasks BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , but they have yet to be directly used for discourse relation prediction in social media. We evaluated our model by comparing it to off-the-shelf end-to-end discourse relation parsers and traditional models. We found that the SVM and random forest classifiers work better than the LSTM classifier for the causality detection, while the LSTM classifier outperforms other models for identifying causal explanation.
The contributions of this work include: (1) the proposal of models for both (a) causality prediction and (b) causal explanation identification, (2) the extensive evaluation of a variety of models from social media classification models and discourse relation parsers to RNN-based application models, demonstrating that feature-based models work best for causality prediction while RNNs are superior for the more difficult task of causal explanation identification, (3) performance analysis on architectural differences of the pipeline and the classifier structures, (4) exploration of the applications of causal explanation to downstream tasks, and (5) release of a novel, anonymized causality Facebook dataset along with our causality prediction and causal explanation identification models.
Related Work
Identifying causal explanations in documents can be viewed as discourse relation parsing. The Penn Discourse Treebank (PDTB) BIBREF7 has a `Cause' and `Pragmatic Cause' discourse type under a general `Contingency' class and Rhetorical Structure Theory (RST) BIBREF8 has a `Relations of Cause'. In most cases, the development of discourse parsers has taken place in-domain, where researchers have used the existing annotations of discourse arguments in newswire text (e.g. Wall Street Journal) from the discourse treebank and focused on exploring different features and optimizing various types of models for predicting relations BIBREF9 , BIBREF10 , BIBREF11 . In order to further develop automated systems, researchers have proposed end-to-end discourse relation parsers, building models which are trained and evaluated on the annotated PDTB and RST Discourse Treebank (RST DT). These corpora consist of documents from Wall Street Journal (WSJ) which are much more well-organized and grammatical than social media texts BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 .
Only a few works have attempted to parse discourse relations for out-of-domain problems such as text categorizations on social media texts; Ji and Bhatia used models which are pretrained with RST DT for building discourse structures from movie reviews, and Son adapted the PDTB discourse relation parsing approach for capturing counterfactual conditionals from tweets BIBREF4 , BIBREF3 , BIBREF16 . These works had substantial differences to what propose in this paper. First, Ji and Bhatia used a pretrained model (not fully optimal for some parts of the given task) in their pipeline; Ji's model performed worse than the baseline on the categorization of legislative bills, which is thought to be due to legislative discourse structures differing from those of the training set (WSJ corpus). Bhatia also used a pretrained model finding that utilizing discourse relation features did not boost accuracy BIBREF4 , BIBREF3 . Both Bhatia and Son used manual schemes which may limit the coverage of certain types of positive samples– Bhatia used a hand-crafted schema for weighting discourse structures for the neural network model and Son manually developed seven surface forms of counterfactual thinking for the rule-based system BIBREF4 , BIBREF16 . We use social-media-specific features from pretrained models which are directly trained on tweets and we avoid any hand-crafted rules except for those included in the existing discourse argument extraction techniques.
The automated systems for discourse relation parsing involve multiple subtasks from segmenting the whole text into discourse arguments to classifying discourse relations between the arguments. Past research has found that different types of models and features yield varying performance for each subtask. Some have optimized models for discourse relation classification (i.e. given a document indicating if the relation existing) without discourse argument parsing using models such as Naive-Bayes or SVMs, achieve relatively stronger accuracies but a simpler task than that associated with discourse arguments BIBREF10 , BIBREF11 , BIBREF9 . Researchers who, instead, tried to build the end-to-end parsing pipelines considered a wider range of approaches including sequence models and RNNs BIBREF12 , BIBREF15 , BIBREF14 , BIBREF17 . Particularly, when they tried to utilize the discourse structures for out-domain applications, they used RNN-based models and found that those models are advantageous for their downstream tasks BIBREF4 , BIBREF3 .
In our case, for identifying causal explanations from social media using discourse structure, we build an RNN-based model for its structural effectiveness in this task (see details in section UID13 ). However, we also note that simpler models such as SVMs and logistic regression obtained the state-of-the-art performances for text categorization tasks in social media BIBREF18 , BIBREF19 , so we build relatively simple models with different properties for each stage of the full pipeline of our parser.
Methods
We build our model based on PDTB-style discourse relation parsing since PDTB has a relatively simpler text segmentation method; for explicit discourse relations, it finds the presence of discourse connectives within a document and extracts discourse arguments which parametrize the connective while for implicit relations, it considers all adjacent sentences as candidate discourse arguments.
Dataset
We created our own causal explanation dataset by collecting 3,268 random Facebook status update messages. Three well-trained annotators manually labeled whether or not each message contains the causal explanation and obtained 1,598 causality messages with substantial agreement ( $\kappa =0.61$ ). We used the majority vote for our gold standard. Then, on each causality message, annotators identified which text spans are causal explanations.
For each task, we used 80% of the dataset for training our model and 10% for tuning the hyperparameters of our models. Finally, we evaluated all of our models on the remaining 10% (Table 1 and Table 2 ). For causal explanation detection task, we extracted discourse arguments using our parser and selected discourse arguments which most cover the annotated causal explanation text span as our gold standard.
Model
We build two types of models. First, we develop feature-based models which utilize features of the successful models in social media analysis and causal relation discourse parsing. Then, we build a recursive neural network model which uses distributed representation of discourse arguments as this approach can even capture latent properties of causal relations which may exist between distant discourse arguments. We specifically selected bidirectional LSTM since the model with the discourse distributional structure built in this form outperformed the traditional models in similar NLP downstream tasks BIBREF3 .
As the first step of our pipeline, we use Tweebo parser BIBREF20 to extract syntactic features from messages. Then, we demarcate sentences using punctuation (`,') tag and periods. Among those sentences, we find discourse connectives defined in PDTB annotation along with a Tweet POS tag for conjunction words which can also be a discourse marker. In order to decide whether these connectives are really discourse connectives (e.g., I went home, but he stayed) as opposed to simple connections of two words (I like apple and banana) we see if verb phrases exist before and after the connective by using dependency parsing results. Although discourse connective disambiguation is a complicated task which can be much improved by syntactic features BIBREF21 , we try to minimize effects of syntactic parsing and simplify it since it is highly error-prone in social media. Finally, according to visual inspection, emojis (`E' tag) are crucial for discourse relation in social media so we take them as separate discourse arguments (e.g.,in “My test result... :(” the sad feeling is caused by the test result, but it cannot be captured by plain word tokens).
We trained a linear SVM, an rbf SVM, and a random forest with N-gram, charater N-gram, and tweet POS tags, sentiment tags, average word lengths and word counts from each message as they have a pivotal role in the models for many NLP downstream tasks in social media BIBREF19 , BIBREF18 . In addition to these features, we also extracted First-Last, First3 features and Word Pairs from every adjacent pair of discourse arguments since these features were most helpful for causal relation prediction BIBREF9 . First-Last, First3 features are first and last word and first three words of two discourse arguments of the relation, and Word Pairs are the cross product of words of those discourse arguments. These two features enable our model to capture interaction between two discourse arguments. BIBREF9 reported that these two features along with verbs, modality, context, and polarity (which can be captured by N-grams, sentiment tags and POS tags in our previous features) obtained the best performance for predicting Contingency class to which causality belongs.
We load the GLOVE word embedding BIBREF22 trained in Twitter for each token of extracted discourse arguments from messages. For the distributional representation of discourse arguments, we run a Word-level LSTM on the words' embeddings within each discourse argument and concatenate last hidden state vectors of forward LSTM ( $\overrightarrow{h}$ ) and backward LSTM ( $\overleftarrow{h}$ ) which is suggested by BIBREF3 ( $DA = [\overrightarrow{h};\overleftarrow{h}]$ ). Then, we feed the sequence of the vector representation of discourse arguments to the Discourse-argument-level LSTM (DA-level LSTM) to make a final prediction with log softmax function. With this structure, the model can learn the representation of interaction of tokens inside each discourse argument, then capture discourse relations across all of the discourse arguments in each message (Figure 2 ). In order to prevent the overfitting, we added a dropout layer between the Word-level LSTM and the DA-level LSTM layer.
We also explore subsets of the full RNN architecture, specifically with one of the two LSTM layers removed. In the first model variant, we directly input all word embeddings of a whole message to a BiLSTM layer and make prediction (Word LSTM) without the help of the distributional vector representations of discourse arguments. In the second model variant, we take the average of all word embeddings of each discourse argument ( $DA_k=\frac{1}{N_k} \sum _{i=1}^{N_k}W_{i}$ ), and use them as inputs to a BiLSTM layer (DA AVG LSTM) as the average vector of embeddings were quite effective for representing the whole sequence BIBREF3 , BIBREF5 . As with the full architectures, for CP both of these variants ends with a many-to-one classification per message, while the CEI model ends with a sequence of classifications.
Experiment
We explored three types of models (RBF SVM, Linear SVM, and Random Forest Classifier) which have previously been shown empirically useful for the language analysis in social media. We filtered out low frequency Word Pairs features as they tend to be noisy and sparse BIBREF9 . Then, we conducted univariate feature selection to restrict all remaining features to those showing at least a small relationship with the outcome. Specifically, we keep all features passing a family-wise error rate of $\alpha = 60$ with the given outcome. After comparing the performance of the optimized version of each model, we also conducted a feature ablation test on the best model in order to see how much each feature contributes to the causality prediction.
We used bidirectional LSTMs for causality classification and causal explanation identification since the discourse arguments for causal explanation can show up either before and after the effected events or results and we want our model to be optimized for both cases. However, there is a risk of overfitting due to the dataset which is relatively small for the high complexity of the model, so we added a dropout layer (p=0.3) between the Word-level LSTM and the DA-level LSTM.
For tuning our model, we explore the dimensionality of word vector and LSTM hidden state vectors of discourse arguments of 25, 50, 100, and 200 as pretrained GLOVE vectors were trained in this setting. For optimization, we used Stochastic Gradient Descent (SGD) and Adam BIBREF23 with learning rates 0.01 and 0.001.
We ignore missing word embeddings because our dataset is quite small for retraining new word embeddings. However, if embeddings are extracted as separate discourse arguments, we used the average of all vectors of all discourse arguments in that message. Average embeddings have performed well for representing text sequences in other tasks BIBREF5 .
We first use state-of-the-art PDTB taggers for our baseline BIBREF13 , BIBREF12 for the evaluation of the causality prediction of our models ( BIBREF12 requires sentences extracted from the text as its input, so we used our parser to extract sentences from the message). Then, we compare how models work for each task and disassembled them to inspect how each part of the models can affect their final prediction performances. We conducted McNemar's test to determine whether the performance differences are statistically significant at $p < .05$ .
Results
We investigated various models for both causality detection and explanation identification. Based on their performances on the task, we analyzed the relationships between the types of models and the tasks, and scrutinized further for the best performing models. For performance analysis, we reported weighted F1 of classes.
Causality Prediction
In order to classify whether a message contains causal relation, we compared off-the-shelf PDTB parsers, linear SVM, RBF SVM, Random forest and LSTM classifiers. The off-the-shelf parsers achieved the lowest accuracies ( BIBREF12 and BIBREF13 in Table 3 ). This result can be expected since 1) these models were trained with news articles and 2) they are trained for all possible discourse relations in addition to causal relations (e.g., contrast, condition, etc). Among our suggested models, SVM and random forest classifier performed better than LSTM and, in the general trend, the more complex the models were, the worse they performed. This suggests that the models with more direct and simpler learning methods with features might classify the causality messages better than the ones more optimized for capturing distributional information or non-linear relationships of features.
Table 4 shows the results of a feature ablation test to see how each feature contributes to causality classification performance of the linear SVM classifier. POS tags caused the largest drop in F1. We suspect POS tags played a unique role because discourse connectives can have various surface forms (e.g., because, cuz, bcuz, etc) but still the same POS tag `P'. Also POS tags can capture the occurrences of modal verbs, a feature previously found to be very useful for detecting similar discourse relations BIBREF9 . N-gram features caused 0.022 F1 drop while sentiment tags did not affect the model when removed. Unlike the previous work where First-Last, First3 and Word pairs tended to gain a large F1 increase for multiclass discourse relation prediction, in our case, they did not affect the prediction performance compared to other feature types such as POS tags or N-grams.
Causal Explanation Identification
In this task, the model identifies causal explanations given the discourse arguments of the causality message. We explored over the same models as those we used for causality (sans the output layer), and found the almost opposite trend of performances (see Table 5 ). The Linear SVM obtained lowest F1 while the LSTM model made the best identification performance. As opposed to the simple binary classification of the causality messages, in order to detect causal explanation, it is more beneficial to consider the relation across discourse arguments of the whole message and implicit distributional representation due to the implicit causal relations between two distant arguments.
Architectural Variants
For causality prediction, we experimented with only word tokens in the whole message without help of Word-level LSTM layer (Word LSTM), and F1 dropped by 0.064 (CP in Table 6 ). Also, when we used the average of the sequence of word embeddings of each discourse argument as an input to the DA-level LSTM and it caused F1 drop of 0.073. This suggests that the information gained from both the interaction of words in and in between discourse arguments help when the model utilizes the distributional representation of the texts.
For causal explanation identification, in order to test how the LSTM classifier works without its capability of capturing the relations between discourse arguments, we removed DA-level LSTM layer and ran the LSTM directly on the word embedding sequence for each discourse argument for classifying whether the argument is causal explanation, and the model had 0.061 F1 drop (Word LSTM in CEI in Table 6 ). Also, when we ran DA-level LSTM on the average vectors of the word sequences of each discourse argument of messages, F1 decreased to 0.818. This follows the similar pattern observed from other types of models performances (i.e., SVMs and Random Forest classifiers) that the models with higher complexity for capturing the interaction of discourse arguments tend to identify causal explanation with the higher accuracies.
For CEI task, we found that when the model ran on the sequence representation of discourse argument (DA AVG LSTM), its performance was higher than the plain sequence of word embeddings (Word LSTM). Finally, in both subtasks, when the models ran on both Word-level and DA-Level (Full LSTM), they obtained the highest performance.
Complete Pipeline
Evaluations thus far zeroed-in on each subtask of causal explanation analysis (i.e. CEI only focused on data already identified to contain causal explanations). Here, we seek to evaluate the complete pipeline of CP and CEI, starting from all of test data (those or without causality) and evaluating the final accuracy of CEI predictions. This is intended to evaluate CEI performance under an applied setting where one does not already know whether a document has a causal explanation.
There are several approaches we could take to perform CEI starting from unannotated data. We could simply run CEI prediction by itself (CEI Only) or the pipeline of CP first and then only run CEI on documents predicted as causal (CP + CEI). Further, the CEI model could be trained only on those documents annotated causal (as was done in the previous experiments) or on all training documents including many that are not causal.
Table 7 show results varying the pipeline and how CEI was trained. Though all setups performed decent ( $F1 > 0.81$ ) we see that the pipelined approach, first predicting causality (with the linear SVM) and then predicting causal explanations only for those with marked causal (CP + CEI $_{causal}$ ) yielded the strongest results. This also utilized the CEI model only trained on those annotated causal. Besides performance, an added benefit from this two step approach is that the CP step is less computational intensive of the CEI step and approximately 2/3 of documents will never need the CEI step applied.
We had an inevitable limitation on the size of our dataset, since there is no other causality dataset over social media and the annotation required an intensive iterative process. This might have limited performances of more complex models, but considering the processing time and the computation load, the combination of the linear model and the RNN-based model of our pipeline obtained both the high performance and efficiency for the practical applications to downstream tasks. In other words, it's possible the linear model will not perform as well if the training size is increased substantially. However, a linear model could still be used to do a first-pass, computationally efficient labeling, in order to shortlist social media posts for further labeling from an LSTM or more complex model.
Exploration
Here, we explore the use of causal explanation analysis for downstream tasks. First we look at the relationship between use of causal explanation and one's demographics: age and gender. Then, we consider their use in sentiment analysis for extracting the causes of polarity ratings. Research involving human subjects was approved by the University of Pennsylvania Institutional Review Board.
Conclusion
We developed a pipeline for causal explanation analysis over social media text, including both causality prediction and causal explanation identification. We examined a variety of model types and RNN architectures for each part of the pipeline, finding an SVM best for causality prediction and a hierarchy of BiLSTMs for causal explanation identification, suggesting the later task relies more heavily on sequential information. In fact, we found replacing either layer of the hierarchical LSTM architecture (the word-level or the DA-level) with a an equivalent “bag of features” approach resulted in reduced accuracy. Results of our whole pipeline of causal explanation analysis were found quite strong, achieving an $F1=0.868$ at identifying discourse arguments that are causal explanations.
Finally, we demonstrated use of our models in applications, finding associations between demographics and rate of mentioning causal explanations, as well as showing differences in the top words predictive of negative ratings in Yelp reviews. Utilization of discourse structure in social media analysis has been a largely untapped area of exploration, perhaps due to its perceived difficulty. We hope the strong results of causal explanation identification here leads to the integration of more syntax and deeper semantics into social media analyses and ultimately enables new applications beyond the current state of the art.
Acknowledgments
This work was supported, in part, by a grant from the Templeton Religion Trust (ID #TRT0048). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We also thank Laura Smith, Yiyi Chen, Greta Jawel and Vanessa Hernandez for their work in identifying causal explanations.

 Answer the question based on the above article as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Question: What types of social media did they consider?

Answer:


Prompt:
Answer the question based on the given passages. Only give me the answer and do not output any other words.

The following are given passages.
Passage 1:
Georg, Prince of Saxe-Meiningen
Georg, Prince of Saxe-Meiningen (11 October 1892 – 6 January 1946) was the head of the house of Saxe-Meiningen from 1941 until his death.

Biography
He was born in Kassel the eldest son of Prince Frederick Johann of Saxe-Meiningen (1861-1914) and Countess Adelaide of Lippe-Biesterfeld (1870–1948). His father was a son of Georg II, Duke of Saxe-Meiningen and his mother a daughter of Count Ernst of Lippe-Biesterfeld. Georg studied law at the Universities of Munich and Jena.Georg suspended his studies to serve in World War I and saw action as a Captain in a cavalry regiment. His uncle Bernhard III abdicated on 10 November 1918 following the German Revolution as the German monarchies were abolished. After the war he resumed his law studies and for a time served as a substitute judge for the town of Hildburghausen in the Free State of Thuringia. On 1 May 1933 he joined the Nazis, becoming NSDAP member (# 2.594.794).
After the death of his uncle Ernst on 29 December 1941, Georg succeeded to the headship of the house of Saxe-Meiningen and assumed the title of Duke of Saxe-Meiningen and style Georg III.
Georg died in the Russian prisoner of war camp near Cherepovets (Tscherepowetz in German) in Northern Russia. His heir was his second and only surviving son Prince Frederick Alfred who renounced the succession, being a monk in 1953, allowing it to pass to his uncle Bernhard.

Marriage and children
He was married in Freiburg im Breisgau on 22 February 1919 to Countess Klara Marie von Korff genannt Schmising-Kerssenbrock (Darmstadt, 31 May 1895 - Högerhof bei Türnitz, Lower Austria, 10 February 1992). They had four children:

Ancestry
Passage 2:
Ernest II, Duke of Saxe-Gotha-Altenburg
Ernest II, Duke of Saxe-Gotha-Altenburg (Gotha, 30 January 1745 – Gotha, 20 April 1804) was the reigning Duke of Saxe-Gotha-Altenburg from 1772 to 1804. He was the third but second surviving son of Frederick III, Duke of Saxe-Gotha-Altenburg and Luise Dorothea of Saxe-Meiningen. The death of his older brother Frederick in 1756 made him the heir to the duchy of Saxe-Gotha-Altenburg.

Early life
Luise Dorothea was intensely worried about the training of her surviving sons, Ernest and her youngest son August, and had them educated by a select group of teachers. In 1768 and 1769, both princes went on an educational journey to the Netherlands, England and France, and Ernest met important people in politics, science and the arts.

Succession
In 1772 his father died, and Ernest inherited the duchy of Saxe-Gotha-Altenburg. As a liberal and enlightened prince, he was interested in the arts and sciences and used his reign to further them. He promoted the educational system, the economy, theatre, art collections and libraries as well as the natural sciences in his duchy, which was thereby ranked in the top place of the Saxon duchies in Thuringia. Privately, he was particularly interested in astronomy and physics. He appointed competent specialists in all of these areas like the mechanic and clockmaker Johann Andreas Klindworth to whom he granted the title of court mechanic.For his special interests, he employed the services of the important astronomer Franz Xaver von Zach for Gotha. With him, he established the Observatory of Gotha (Sternwarte Gotha), which developed into a European centre of astronomy. His will stated that this institution should survive as the only visible indication of his existence. It was so successful that Gotha, despite its size, was thought of as a place that important people of the time should visit. One such person was Goethe, who visited several times.

Freemasonry
From 1774 he was a Freemason in the Zinnendorf system and a member of the Gotha Lodge Zum Rautenkranz, which had been founded by Abel Seyler, Konrad Ekhof and other members of the Seyler Theatre Company in the same year. In 1775, he was appointed Grand Master of the Landesloge of Germany (Zinnendorf system). In 1783, he became a member of the Bavarian Illuminati under the name of Quintus Severus and/or Timoleon, and in 1784, he was made Supervisor of Abessinien (a name for Upper Saxony). In 1787, he granted Adam Weishaupt, the founder of the secret society, asylum in Gotha. He was buried wrapped in a white cloth on the park island.

Descendants
In Meiningen on 21 March 1769, Ernest married Princess Charlotte of Saxe-Meiningen, the half-first cousin of his mother. They had four sons:

Ernest (b. Gotha, 27 February 1770 – d. Gotha, 3 December 1779).
Emil Leopold August, Duke of Saxe-Gotha-Altenburg (b. Gotha, 23 November 1772 – d. Gotha, 27 May 1822), known as Augustus.
Frederick IV, Duke of Saxe-Gotha-Altenburg (b. Gotha, 28 November 1774 – d. Gotha, 11 February 1825).
Ludwig (b. Gotha, 21 October 1777 – d. Gotha, 26 October 1777).

Ancestors
Passage 3:
Karl Wilhelm, Duke of Saxe-Meiningen
August Friedrich Karl Wilhelm, Duke of Saxe-Meiningen (Frankfurt, 19 November 1754 – Sonneberg, 21 July 1782), was a duke of Saxe-Meiningen.

Family
He was the first son of Anton Ulrich, Duke of Saxe-Meiningen and Charlotte Amalie of Hesse-Philippsthal.

Reign
August Friedrich succeeded his father in the duchy of Saxe-Meiningen (1763) when he was only eight years old. Because of this, his mother, the Dowager Duchess Charlotte Amalie, acted as regent during his minority, which ended in 1779. He was succeeded by his last younger surviving brother, Georg.

Marriage
In Gedern on 5 June 1780, Karl Wilhelm married Louise of Stolberg-Gedern. They had no children. The widowed Louise later married Duke Eugen of Württemberg and had issue.

Ancestry
Passage 4:
Princess Feodora of Saxe-Meiningen
Princess Feodora of Saxe-Meiningen (Feodora Viktoria Auguste Marie Marianne; 12 May 1879 – 26 August 1945) was born at Potsdam, the only child of Bernhard III, Duke of Saxe-Meiningen, and his wife, Princess Charlotte of Prussia (the eldest daughter of Frederick III, German Emperor, and Victoria, Princess Royal). Feodora was the first great-grandchild of both Queen Victoria of Great Britain and Emperor William I of Germany.

Early life
Princess Feodora was born on 12 May 1879 as the only child of Bernhard, Hereditary Prince of Saxe-Meiningen, and his wife Princess Charlotte of Prussia, herself the eldest daughter of German Crown Prince Frederick William and Crown Princess Victoria. The new baby was the first grandchild of the Crown Prince and Princess, and through her mother was also the first great-grandchild of the British Queen Victoria.Charlotte, who loved to socialize, had hated being pregnant, believing that it limited her activities. Preferring to return to enjoying social life in Berlin, she declared after Feodora's birth that she would have no further children, dismaying her mother, Crown Princess Victoria. It was unusual to be an only child in European royal families, and Feodora likely endured a lonely childhood. Charlotte loved to travel, and often left her daughter with Vicky at Friedrichshof, whom she viewed as the source of a convenient nursery. The Crown Princess, for her part, loved having the chance to spend time with her eldest granddaughter. Describing Feodora on one visit, she wrote that "she is really a good little child, & far easier to manage than her Mama".Victoria, who became German empress in 1888, perceived a deficit in Feodora's upbringing and gradually became concerned about the girl's physical appearance and mental development, describing the thirteen-year-old as possessing "sharp pinched features" and an unusually short stature. Feodora also cared little for her studies, preferring instead to discuss fashion. Her grandmother, who placed a high value on education, blamed insufficient parental guidance for the girl's lack of studiousness, commenting that the "atmosphere of her home is not the best for a child of her age... With Charlotte for an example, what else can one expect... Her parents are rarely ever at home or together... She hardly knows what home life is!"Queen Victoria was fond of her eldest great-grandchild. In June 1887, the young Feodora and her parents attended the queen's  Golden Jubilee in London. While her parents stayed at Buckingham Palace, Feodora stayed with her young cousin Princess Alice of Battenberg at the home of the Dowager Duchess of Buccleuch at Whitehall, allowing the girls to watch the royal procession as it made its way to Westminster Abbey. Queen Victoria described her as "sweet little Feo, who is so good and I think grown quite pretty. We were delighted to have her and I think the dear child has enjoyed herself."

Marriage
As Feodora grew older, her marriage began to be a consideration. The exiled Prince Peter Karađorđević, thirty-six years older than Feodora, proposed himself as a suitor, though this was likely a bid to gain support for succeeding to the Serbian throne. Charlotte declared that "for such a throne Feodora is far too good". Her mother's maternal first cousin Alfred, Hereditary Prince of Saxe-Coburg and Gotha, the only son of Charlotte's friend (and Feodora's maternal grandaunt) the Duchess of Saxe-Coburg and Gotha, was also considered.Several months after returning from Queen Victoria's Diamond Jubilee celebrations in June 1897, Feodora became engaged to Prince Heinrich XXX Reuss of Köstritz (1864-1939), with the betrothal announced in early October. Born in Castle Neuhoff, he was the youngest son of Prince Heinrich IX Reuss of Köstritz (1827-1898) and Baroness Anne Marie Wilhelmine Helene of Zedlitz und Leipe (1829-1907). Henry's father died in early 1898, forcing a temporary postponement of the marriage. Rumours that the marriage had been cancelled proved to be untrue, and they married at Breslau on 24 September 1898 in  a Lutheran ceremony. Princess Feodora was the only great-grandchild of Queen Victoria and only grandchild of German Empress Victoria to be married in their lifetimes; she married in 1898 and both the Queen and the German Empress died in 1901.
Prince Henry was a captain in the Brunswick Infantry Regiment No. 92, though not particularly wealthy or high-ranked. Feodora's grandmother Empress Victoria was surprised at the choice of groom, particularly his lack of position, but observed that the bride at least seemed happy. Of the fifteen-year age gap, Victoria commented, "I am very glad he is older than she is, and if he is wise and steady and firm, he may do her a vast deal of good, and it may turn out very well, but she has had a strange example in her mother, and is a strange little creature." The historian John Van der Kiste writes that Feodora was "evidently besotted" with her new husband, and she likely also sought marriage as an escape from her "irksome home life".Once returned from their honeymoon, Henry spent much of his time on duty with his regiment, while Feodora joined a reading group and attended the opera and theatre in Berlin. Feodora also often accompanied her husband during his military assignments, travelling throughout Germany.

Last years
Feodora suffered most of her adult life from ill health, describing it as "the old story" of her life. Like her mother and maternal grandmother, and maternal great grandmother Feodora's illnesses included dizziness, insomnia, nausea, various pains, paralysis, constipation, and diarrhoea. She underwent several operations to treat her illnesses and alleviate her infertility, each without success.Feodora visited Windsor Castle in 1900, which would be the last time she saw her great-grandmother before Queen Victoria's death the following year. Henry attended her funeral, but ill health kept Feodora from attending. Feodora blamed malaria for her condition, though Charlotte told family members that Henry had given his wife a venereal disease, an allegation Feodora furiously denied. Charlotte asked her daughter to get tested by Charlotte's personal physician; when Feodora refused, it confirmed to Charlotte that her beliefs were correct. In reaction, Feodora refused to enter her mother's house and complained to family members of Charlotte's "incredible" actions.In 1903, the couple moved to Flensburg upon Henry being transferred, where they lived in a small house. Feodora found that the region's mild climate had a positive impact on her health. To further improve it and increase the probability of becoming pregnant, she took pills of arsenic and thorium. Her poor health recurred, however, and she again began suffering from toothache and migraines. In October 1904, a serious illness was blamed on influenza. Her further efforts to conceive included numerous visits to private clinics through the years, which often led to painful surgeries and procedures.

Two World Wars
With the outbreak of World War I, Henry was dispatched to the Western Front, while his wife opened a small hospital to treat wounded soldiers. By this stage, relations between him and his wife had deteriorated; Henry believed Feodora enjoyed complaining about being sick and seeing doctors. He wrote that her illness "consists mainly in complete lack of energy and mental apathy", and complained that "she grossly exaggerates her illnesses and causes me and others quite unnecessary anxiety". Henry died in 1939.
After the war concluded with Germany's defeat, Feodora's father's rule over the Duchy of Saxe-Meiningen was ended. Her post-war life is mostly unknown, and records of her subsequent medical history have mainly been lost. She spent her final years at the Sanatorium Buchwald-Hohenwiese, near Hirschberg, Silesia, in what is now southwestern Poland. She died by suicide on 26 August 1945, dying shortly after World War II ended. In describing Feodora's life, the historian John Van der Kiste writes that "the princess who had so desperately wanted children of her own had instead continued to battle with constant physical ailments, insomnia and severe depression, and endured many years of ill-health similar to that of her mother".

Medical analysis
In the 1990s, the historian John Röhl and his colleagues Martin Warren and David Hunt found Feodora's grave in Poland, exhuming the body for DNA analysis in the belief that it would reveal signs of the genetic disease porphyria however, it was proved inconclusive.

Ancestry
Passage 5:
Princess Charlotte of Saxe-Meiningen
Princess Charlotte of Saxe-Meiningen (German: Marie Charlotte Amalie Ernestine Wilhelmine Philippine, Prinzessin von Sachsen-Meiningen) (11 September 1751, Frankfurt am Main, Free Imperial City of Frankfurt, Holy Roman Empire – 25 April 1827, Genoa, Kingdom of Sardinia) was a member of the House of Saxe-Meiningen and a Princess of Saxe-Meiningen by birth and a member of the House of Saxe-Gotha-Altenburg and Duchess consort of Saxe-Gotha-Altenburg through her marriage to Ernest II, Duke of Saxe-Gotha-Altenburg.

Early life and family
Princess Charlotte was born on
11 September 1751. She was the eldest child and daughter of Anton Ulrich, Duke of Saxe-Meiningen and his second wife, Landgravine Charlotte Amalie of Hesse-Philippsthal. Charlotte was an elder sister of Charles William, Duke of Saxe-Meiningen and George I, Duke of Saxe-Meiningen.

Marriage
Charlotte married Ernest, Hereditary Prince of Saxe-Gotha-Altenburg (later Duke of Saxe-Gotha-Altenburg), son of Frederick III, Duke of Saxe-Gotha-Altenburg and his wife Luise Dorothea of Saxe-Meiningen, on 21 March 1769 in Meiningen. Charlotte and Ernest had four children:

Ernest, Hereditary Prince of Saxe-Gotha-Altenburg (b. Gotha, 27 February 1770 – d. Gotha, 3 December 1779).
Augustus, Duke of Saxe-Gotha-Altenburg (b. Gotha, 23 November 1772 – d. Gotha, 27 May 1822)
Frederick IV, Duke of Saxe-Gotha-Altenburg (b. Gotha, 28 November 1774 – d. Gotha, 11 February 1825).
Prince Ludwig of Saxe-Gotha-Altenburg (b. Gotha, 21 October 1777 – d. Gotha, 26 October 1777).Charlotte's husband, Ernest, was regarded as an enlightened monarch and a great patron of art and science, who led his country into a cultural flowering. He was assisted in his cultural undertakings by his wife, Charlotte.
Like her husband, Charlotte was a patron of astronomy. She counted relief panels for the court astronomer Franz Xaver von Zach and she also participated in observations. Charlotte also participated in the First European Astronomy Congress in 1798 at the Seeberg Observatory and independently corresponded with the leading astronomers of her time.

Later life
After her husband's death in 1804, there were difficulties with Charlotte's son, Augustus, upon his succession. Charlotte left Gotha with Zach and spent some time in Eisenberg. Later she traveled with Zach throughout southern Europe and lived several years in Marseilles, and later in Genoa, where she died in 1827.

Ancestry
Passage 6:
Bernhard, Prince of Saxe-Meiningen
Bernhard, Prince of Saxe-Meiningen (German: Bernhard, Prinz von Sachsen-Meiningen; 30 June 1901 – 4 October 1984) was the head of the House of Saxe-Meiningen from 1946 until his death.

Prince of Saxe-Meiningen
Bernhard was born in Köln the third son of Prince Frederick Johann of Saxe-Meiningen and Countess Adelaide of Lippe-Biesterfeld. His father was the second son of Georg II, Duke of Saxe-Meiningen and his mother a daughter of Count Ernst of Lippe-Biesterfeld.
After the death of his older brother Prince Georg in 1946 his nephew Prince Frederick Alfred renounced his succession rights and so Bernhard succeeded to the headship of the house of Saxe-Meiningen and the nominal title of Duke of Saxe-Meiningen (as Bernhard IV).
As his first marriage was morganatic his second son Prince Frederick Konrad succeeded him as head of the ducal house following his death in Bad Krozingen.
Bernhard and his first wife were declared guilty of a Nazi conspiracy against Austria in 1933; he was sentenced to six weeks in prison, while she was placed under house arrest. After intervention of the German envoy, he was released from prison, upon which they escaped to Italy. Three weeks later he was arrested while trying to return to his castle of Pitzelstaetten

Family
Bernhard was married morganatically to Margot Grössler (1911–1998), a merchant's daughter from Breslau (today: Wrocław) in Eichenhof im Riesengebirge on 25 April 1931. This union ended in divorce on 10 June 1947. They had two children, both of whom had no succession rights:

Princess Feodora of Saxe-Meiningen (27 April 1932) she married Burkhard Kippenberg on 6 April 1967. They have one son:
Walter Johannes Kippenberg (27 January 1968)
Prince Frederick Ernest of Saxe-Meiningen  (21 January 1935 – 13 July 2004) he married Ehrengard von Massow on 3 March 1962. He remarried Princess Beatrice of Saxe-Coburg and Gotha on 12 June 1977. They have two children and one grandson:
Princess Marie Alexandra of Saxe-Meiningen (5 July 1978) married Benno Beat Christian Wiedmer on 27 July 2004.
Prince Friedrich Constantin of Saxe-Meiningen (3 June 1980) He has one son with Sophia Lupus:
Michael of Saxe-Meiningen (July 2015)Bernhard married secondly in Ziegenberg über Bad Nauheim on 11 August 1948 to Baroness Vera Schäffer von Bernstein (1914–1994). They had three children, whose son Konrad with full rights to the succession to the house of Saxe-Meiningen:

Princess Eleonore Adelaide of Saxe-Meiningen (9 November 1950) she married Peter Eric Rosden on 22 October 1982.
Prince Frederick Konrad of Saxe-Meiningen (14 April 1952)
Princess Almut of Saxe-Meiningen (25 September 1959) she married Eberhard von Braunschweig on 16 October 1993. They have two children:
Marie Cecilie von Braunschweig (4 August 1994)
Julius-Alexander von Braunschweig (20 October 1996)

Ancestry
Passage 7:
Princess Marie Elisabeth of Saxe-Meiningen
Princess Marie Elisabeth of Saxe-Meiningen (23 September 1853 – 22 February 1923) was the only daughter of Georg II, Duke of Saxe-Meiningen, by his first wife, Princess Charlotte of Prussia. She was notable as a musician and composer. One of her most famous works is Romanze in F major for clarinet and piano.

Early life
Princess Marie Elisabeth was born on 23 September 1853 in Potsdam. She was the third child and only daughter of Georg, Hereditary Prince of Saxe-Meiningen, by his first wife, Princess Charlotte of Prussia, and had one surviving brother, Hereditary Prince Bernhard. Her parents' marriage was very happy, as it was the rare instance of a love match rather than a marriage of state. In 1855, tragedy struck with the death of their younger infant brother; Charlotte died three months later of complications after childbirth of a baby son, two months earlier. This youngest brother died, one day old. Georg was inconsolable, but remarried several years later to Princess Feodora of Hohenlohe-Langenburg in order to provide a mother for his remaining young children. Marie Elisabeth's father succeeded as Duke Georg II of Saxe-Meiningen in 1866. His second marriage was unhappy; it produced three younger brothers (Ernst, Friedrich, and Viktor) for Marie Elisabeth before Feodora's death in 1872.Marie Elisabeth's father participated in the Franco-Prussian War, where he fought in nearly every battle. After the war, Georg II devoted himself to the stage, and his court became famous for its brilliance and culture. A year after Feodora's death, Georg II married for a third and last time to Ellen Franz, a stage actress. A happy marriage, together they founded the Meiningen Ensemble, which became the centre for dramatic art in Germany.

Music
As her father was a great patron of the stage and the founder of a national theater, Marie Elisabeth was raised in this environment, consequently becoming artistic and a great lover of music like her parents. She received a thorough education under the tutelage of Theodor Kirchner, a talented pianist. Her father was a great patron of German composer and pianist Johannes Brahms, who worked as a music teacher in Meiningen for various pupils, including Marie Elisabeth, whom he gave piano lessons to. In addition to Brahms, Marie Elisabeth was in close contact with other famous musicians, such as Richard Strauss, Franz Mannstädt, and Hans von Bülow.
Marie Elisabeth was a student of the Conservatorium; she and Prince Alexander of Hesse, another royal pupil of Brahms, celebrated the birthday of musician Joachim Raff in Frankfurt in 1886. There, they interpreted Brahm's Sonata (Op. 78) for pianoforte and violin in a special feature for the ceremony. At the 1878 wedding of her elder brother Bernhard to Princess Charlotte of Prussia, eldest daughter of German Crown Prince Frederick William, Marie Elisabeth composed a piece of music specially meant for the occasion entitled torch dance.
As of 1913, Marie Elisabeth was the author of Einzugsmarsch for orchestra, Fackeltanz for piano as well as several other piano compositions. She also wrote a "pretty" Cradle Song for violin and piano, and, in 1892, she produced a Romanze in F major for clarinet and piano which had been influenced by Brahms' teachings. At her residence in Berchtesgaden, Marie Elisabeth received a regular circle of artists and encouraged talented singers by financing their education.
Marie Elisabeth died on 22 February 1923 in Obersendling. She never married, and is buried in the cemetery park in Meiningen.

Ancestry
Passage 8:
Princess Louise Eleonore of Hohenlohe-Langenburg
Princess Louise Eleonore of Hohenlohe-Langenburg (11 August 1763, in Langenburg – 30 April 1837, in Meiningen) was a German regent.  She was duchess of Saxe-Meiningen by marriage to George I, Duke of Saxe-Meiningen, and Regent of Saxe-Meiningen during the minority of her son from 1803 to 1821.

Life
Louise Eleonore was a daughter of Prince Christian Albert Louis of Hohenlohe-Langenburg (1726-1789) and his wife Princess Caroline of Stolberg-Gedern (1732–1796).
On 27 November 1782, in Langenburg, she married George I, Duke of Saxe-Meiningen.

Regency
When her husband died on 24 December 1803, she took over as regent of the duchy for their son Bernhard II. She ruled with energy, courage, and good sense during the Napoleonic Wars, which for the next decade ravaged the Saxon states.The duchy was forced to join the Confederation of the Rhine during these Wars and provide it with troops; afterwards the duchy was struck with famine, which Luise sought to prevent by importing wheat.  French armies, and later those of Russia, marched back and forth across the country, but Luise refused to flee; she stayed with her infant son and two daughters inside their castle.She used every strategy to preserve the autonomy of her regency, so that when she joined the Allies in 1813, she had saved the duchy for her son. He became the ruling Duke of Meiningen eight years later.By adjustments in the duchy's administration she ensured the duchy was better managed and in 1821 opened the Gymnasium Bernhardinum in Meiningen (already begun by her husband).
Her children were carefully educated, with a grand tour to Italy under their tutor Johann Heinrich Pestalozzi.  After her son came of age, Luise retired as regent and went on several foreign trips, including one to England to visit her daughter Adelaide.

Issue
Adelheid  (later Adelaide, 13 August 1792 – 2 December 1849), with whom Luise had a very close relationship; in 1818 she married King William IV of the United Kingdom while Luise was regent and special taxes needed to be instituted in the duchy to raise funds for her enormous dowry (6,000 florins per year).
Ida (25 June 1794 – 4 April 1852), married Prince Bernhard of Saxe-Weimar-Eisenach.
Stillborn daughter (16 October 1796).
Bernhard II, Duke of Saxe-Meiningen, (17 December 1800 – 3 December 1882), married Princess Marie Frederica of Hesse-Kassel (1804–1888).

Ancestry
Passage 9:
Friedrich Wilhelm, Duke of Saxe-Meiningen
Friedrich Wilhelm, Duke of Saxe-Meiningen (16 February 1679 in Ichtershausen – 10 March 1746 in Meiningen), was a duke of Saxe-Meiningen.

Life
He was the fifth son of Bernhard I, Duke of Saxe-Meiningen and his first wife, Marie Hedwig of Hesse-Darmstadt.
When his father died in 1706, according to his will, he inherited the duchy of Saxe-Meiningen with his older full-brother, Ernst Ludwig I, and his younger half-brother, Anton Ulrich.
But, shortly after, Ernst Ludwig signed a contract between himself and his brothers, and they were compelled to leave full control of the duchy in his hands.
When Ernst Ludwig died (1724), Friedrich Wilhelm and Anton Ulrich took again the government of the duchy as guardians of his nephews until 1733.
After the death of his nephew, Karl Frederick (1743), he inherited the duchy of Saxe-Meiningen.
Friedrich Wilhelm never married and died after only three years of reigning. He was succeeded by his younger half-brother, Anton Ulrich.

Ancestors
Passage 10:
Bernhard III, Duke of Saxe-Meiningen
Bernhard III (German: Bernhard Friedrich Wilhelm Albrecht Georg; 1 April 1851 – 16 January 1928), was the last reigning duke of Saxe-Meiningen.

Biography
Bernhard was born on 1 April 1851 at Meiningen in what was then the German Confederation,
as the eldest son of Georg II, Duke of Saxe-Meiningen and his first wife Princess Charlotte of Prussia.Bernhard had one full sister, Princess Marie Elisabeth, and several half-brothers by his father's second marriage.
From 1860 Bernhard was schooled by a Prof. Rossmann before he went to study at Heidelberg University in 1869. For the war against France he interrupted his studies and served as Ordonnanz-Offizier. After the war ended he resumed his studies at Leipzig. From 1873 he again served in the military and rose into the highest echelons: By 1905 he was Generaloberst and inspector general of the 2nd Army Inspection (Central Germany). In 1909, he became Generaloberst im Range eines Generalfeldmarschalls and retired from active service in 1912.

He married in Berlin on 18 February 1878 Princess Charlotte of Prussia, his second cousin, daughter of Frederick III, German Emperor and granddaughter of the Queen Victoria. They had one daughter: Princess Feodora of Saxe-Meiningen (b. Potsdam, 12 May 1879 - d. Schloß Neuhoff, 26 August 1945), married on 24 September 1898 to Heinrich XXX of Reuss-Köstritz.

Reign
Bernhard assumed the Duchy of Saxe-Meiningen after the death of his father in 1914. With the start of World War I Bernhard hoped to be assigned command over an army but was disappointed. In reaction he also withdrew from his role in the Duchy's government.After Germany lost the war, the German revolution forced Bernhard to abdicate as duke on 10 November 1918. Like all the German princes he lost his title and state. He spent the rest of his life in his former country as a private citizen.Bernhard died on 16 January 1928 in Meiningen. He is buried next to his wife in the park at Altenstein.

Interests
Despite his military career he also took a great interest in the arts. He was active as a composer, poet and translator. He was known in particular as an expert on Modern Greek and was renowned for translating German literature into Greek. For his historical studies, for which he repeatedly travelled to Greece and Asia Minor, the University of Breslau awarded him an honorary doctorate.

Honours
Ernestine duchies: Grand Cross of the Saxe-Ernestine House Order, 1869; Joint Grand Master, 25 June 1914
 Kingdom of Prussia:Iron Cross (1870), 2nd Class
Grand Commander's Cross of the Royal House Order of Hohenzollern, 2 April 1877
Knight of the Order of the Black Eagle, 23 April 1877; with Collar, 1878
 Saxe-Weimar-Eisenach: Grand Cross of the Order of the White Falcon, 1870
 Grand Duchy of Hesse: Grand Cross of the Grand Ducal Hessian Order of Ludwig, 18 February 1878
 Belgium: Grand Cordon of the Order of Leopold (military division), 25 February 1878
 Oldenburg: Grand Cross of the House and Merit Order of Duke Peter Friedrich Ludwig, with Crown in Gold, 18 February 1878
 Baden:Knight of the House Order of Fidelity, 1881
Knight of the Order of Berthold the First, 1881
 Kingdom of Saxony: Knight of the Order of the Rue Crown, 1885
  Austria-Hungary: Grand Cross of the Royal Hungarian Order of St. Stephen, 1887
 United Kingdom: Honorary Knight Grand Cross of the Most Honourable Order of the Bath (civil division), 21 June 1887
 Empire of Japan: Grand Cordon of the Supreme Order of the Chrysanthemum, 5 February 1896
 Russian Empire: Knight of the Imperial Order of St. Alexander Nevsky, 1896

Ancestry

Answer the question based on the given passages. Only give me the answer and do not output any other words.

Question: What nationality is Princess Charlotte Of Saxe-Meiningen's husband?
Answer: