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A connective differentiation of textual production in interaction networks

This paper explores textual production in interaction networks, with special emphasis on its relation to topological measures. Four email lists were selected, in which measures were taken from the texts participants wrote. Peripheral, intermediary and hub sectors of these networks were observed to have discrepant linguistic elaborations. For completeness of exposition, correlation of textual and topological measures were observed for the entire network and for each connective sector. The formation of principal components is used for further insights of how measures are related.


I. INTRODUCTION
Textual production has received considerable attention from the social network analysis community. Sentiment analysis and vocabularies related to different parties are among a number of examples 10 . The relation of topological and textual measures is the subject of this article, for the following reasons: • This relation has been set aside in literature, with scattered and vague suggestions of mutual implications of the text produced and topological characteristics of the agents in the network 10 .
• This results eases understanding of human interaction, which is useful for both psychological and anthropological typologies (personality and cultural "types") 7 .
• There are interesting hypothesis about verbal differentiation of network sections and groups, derived from a previous article by the same author 11 , some of which were herein confirmed.
Next section exposes materials used for this research, its textual and network facets. Section III explains the analysis roadmap, with the measures chosen and methods for understanding data. Section IV is dedicated to detailing results and discussion. Section V has concluding remarks and further works envisioned. Appendices hold information about mailing lists, tables, results still to interpret and directions on data and scripts.

II. MATERIALS
Eighty thousand messages were analysed, twenty thousand from each email list (see Appendix B). This data was accessed online through the GMANE database 14 . Each message has an ID, the ID of the message it is a a) Electronic mail: fabbri@usp.br response to (if any), an author, a "date and time" field registering the moment the message was sent, and the textual content. Other fields are also available, but plays no direct role in the work here presented. Basic information of messages, threads and authors are summarized in Table I. Message-response pairs yield interaction networks, such as shown in Figure 1. Each participant is represented by a vertex, and each response is considered evidence that information emitted by the first author was received by the responder (that had to read, process its contents and render a relevant textual response). Therefore, an edge from first author to the second author (responder) is considered. This is the "information network" of the system. Edges can be considered in the reverse order, from the responder to the original sender, representing status attribution, as the responder considered what the sender said worthy of responding and is directing his attention to him. This is the "status network".
As these networks are virtually equivalent, one considers but one of them, usually the information network.

B. Verbal observations
Each message has a textual content. Analysis this content can consider author, network section or community, or messages independently. As these are informal communities, there are typos, leet and invented words. This diversity and informality poses some challenges, by which the methodology was shaped.

III. METHODOLOGY
An article was written for reporting stability in such networks from the topological viewpoint 11 . This article is dedicated to reporting differentiation in the textual production of the network as connectivity changes. Here, the observance of primary textual statistics is needed, and both overall incidences, and correlation to topological aspects, were tackled.
It is coherent to have participants as vertexes and as references for the messages sent, for the text produced and for activity (related to time and date). Thus, to observe the text produced in a certain section, one might gather all text produced by all participants on that section. To observe correlation of textual and topological characteristics, one can take measures on each vertex.

A. Network measurements and partitioning
Basic network measures of connectivity, in the same networks, were observed in a previous article 11 . The present article uses the same topological measures to observe correlations, PCA formation and network sectioning in peripheral, intermediary and hub sectors, through strength measure. As described in that article, the "exclusivist criteria" for such partitioning is found to be the closest to literature predictions (5% of hubs, 15% of intermediary and 80% of peripheral vertex). Even so, strength-based criteria is simpler and yields reasonable results (5-10%, 5-25%, 65-90%). Beyond that, changing the sectioning to a degree or a compound criteria did not significantly change the presented results.
Consequently, herein is considered a strength partitioning each sector (periphery, intermediary, hubs) is regarded as a primitive sector of the primitive partitioning.

B. Textual measures
An infinitude of textual measures can be drawn from texts. This work focuses on the simplest of them, as they proved sufficient for current step. These measures include frequency of individual letters and punctuations (Tables II), of words and tokens (Table III), sizes of tokens, sentences and messages (Table IV, V and VI) and POS (Part-Of-Speech) tags (Table VII). Other measures envisioned are in subsection V A.
This choice is based on: 1) the lack of such information in literature, as far as authors know; 2) potential relations of these incidences with topological aspects, such as connectivity; 3) the interdependence of textual artifacts suggests that simple measures should reflect complex behaviors subtle aspects. A preliminary study, with all the work from Machado de Assis 13 , made clear that these measures vary with respect to style.
Considered measures are: • Frequency of characters: letters, vowels, punctuations and uppercase. Table II is dedicated to such measures.
• Number of tokens, frequency of punctuations, of known words, of words that has wordnet synsets, of tokens that are stopwords, of words that return synsets and are stop words, etc. Table III is dedicated to measures of this kind.
• Mean and standard deviation for word and token sizes. Table IV is dedicated to these measures.
• Mean and standard deviation of sentence sizes. Table V is dedicates to this sort of measures.
• Mean and standard deviation of message sizes. Table VI presents some of these measures.
• Fraction of morphosyntactic classes, such as adverbs, adjectives and nouns, represented by POS (Part-Of-Speech) tags. Table VII displays such  measures. For sections (hubs, intermediary and peripheral), all messages written by authors in each section were considered together. For the histograms, independent messages were considered from each sector.

C. Topological measures
Degree (in, out and total), strength (in, out and total), betweenness centrality and clustering coefficient were measured for each vertex in the interaction network. This served two purposes: • Obtaining a sound partitioning of the network in peripheral, intermediary and hub sectors. This was developed in a previous article by the same author 11 .
• Observance of correlation with textual measures and principal components formation.
These measures are not developed here extensively as they are very consolidated, simple, and was the core of a previous article this subject by the same author 11 .

D. Relating text and topology
Key observations for a deeper insight about network structure depend on theoretical background and intentions. For this article, were considered: 1. Incidences of linguistic traces in hub, intermediary and peripheral network sectors.
2. Correlation of measures of each vertex, easing pattern detection involving topology of interaction and language used.
3. PCA to gain further insights about how measures combine in principal components formation.
Criteria for this choice include integration with previous topological results, lack of concise results in literature (as far as author knows) that could substantiate correlations of topological and textual traces, and common sense as a long-time member of these networks.
First task, of textual production in hubs, intermediary and peripheral sectors, is observed by Tables II-XV. An adaptation of the Kolmogorov-Smirnov test was used to observe differences in textual productions, as exposed in Appendix A.
Second task is addressed by the correlation matrix with both textual and topological measurements of each participant, in Tables XVI-XVIII. Third, principal components composition are in Tables XIX-XXIII.

IV. RESULTS AND DISCUSSION
Although the results drawn from experiments and statistics were diverse, some fundamental insights can be obtained by going through tables and figures in the Appendix B. Most importantly: connectivity has strong influence in textual production of participants in the network. For example: hubs use more contractions, more adjectives, more common words, and less punctuation if compared to the rest of the network, specially the peripheral sector. In general, rise or fall of a measure was monotonic, but some of them reached extreme values in the intermediary sector.
Next subsections exhibit particular results of interests.

A. General characteristics of activity distribution among participants
Hubs and periphery swap fraction of participants and activity. While peripheral sector has ≈ 75% of participants, it produces ≈ 10% of all messages. Conversely, hubs has ≈ 10% of participants and produces ≈ 75% of all messages. Fewer threads are created in proportion to total messages sent by the hubs, while threads created by peripheral are twice as frequent as general peripheral messages. This suggests a symbiosis of peripheral diversity and hub activity.
Also, comparing lists with a fixed number of messages, the number of threads created seem to increase as the number of participants decrease. These information is condensed in Table I, with further details.

B. Characters
Peripheral vertex use more punctuation characters, digits and uppercase letters. Hubs use more letters and vowels among letters. The use of space does not seem to have any relation to connectivity, with the exception that the intermediary presented a slightly lower incidence of spaces than both peripheral and hub sectors.
Total number of characters in ELE list, in the 20 thousand messages, is more than three times what other lists exhibited. This suggests peculiarities related to communication conventions and style (see Appendix B 1).
Further information is given in Table II.

C. Tokens and words
Largest average size of tokens is with the most wordy list (ELE). This implies that is has more characters, tokens, and characters per token in comparison to the other lists. Longer words used by hubs might be related to the use of a specialized vocabulary. Although the token diversity ( |tokens =| |tokens| ) found in peripheral sector is far greater, this result has the masking artifact that the peripheral sector corpus is smaller, yielding a larger token diversity. This can be noticed by the token diversity of the whole network, which is lower than in the sections. This same discussion applies to the lexical diversity ( |kw =| kw ). Punctuations among tokens are less abundant in hubs, and discrepancies here are larger that with characters comparisons (subsection IV B). Known words are used more frequently by hubs.
ELE and CPP both exhibit intermediaries with the more frequent production of punctuation, less frequent production of known words, the highest incidence of words with wordnet synsets among known words. This suggests some peculiarity in network structure, such as the intermediary be strong authorities in such networks, using smaller sentences and a larger jargon.
Words with synsets, among known English words, are less frequent in hubs further evidencing the jargon hubs develop.
Further information is given in Table III.

D. Sizes of tokens and words
Sizes of known words are smaller for hubs, which suggests its use of more common words, although some of the previous results suggests that hubs have a very differentiated and specialized vocabulary. Larger words seems to be related to intermediary sector, which might be related to cultured vocabulary.
Further information is given in Table IV.

E. Sizes of sentences
Hubs present the lowest average sentence size, both in characters and in tokens. Also, the incidence of usual known words seems to decay with connectivity, as does the number of known words with synsets.
Further information is given in Table V.

F. Messages
Regarding characters and tokens, connectivity was related to smaller messages. ELE list displayed an inverse situation: the more connected the sector, the longer the messages are. This was considered a peculiarity of the culture bonded with the political subject of ELE list, to be further verified. Regarding sentences, the size of messages seem to hold steady until hubs are reached.
Further information is given in Table VI.

G. POS tags
Lower connectivity delivers more nouns and less adjectives, adverbs and verbs. This suggests that the networks collect issues important to the world by the peripheral sector. These issues are qualified, elaborated about, by the more connected participants.
Further information is given in Table VII.

H. Differentiation of measures
The Kolmogorov-Smirnov test was adapted for our need to compare measures. Results suggests that the texts produced by each sector is very distinct. Counterintuitively, intermediary sector sometimes yields a greater difference from periphery and hubs than these extreme sectors themselves (Tables VIII and XII).
At the core of the results presented on this article, are two strong and immediate interpretations that follows Tables VIII-XV: • Differences of textual production of the primitive sectors are extreme, as can be noticed from the values on these tables, beyond reference values used for considering the null hypothesis (see Appendix A).
• Differences between sectors on the same network (Tables VIII, X, XII and XIV) are bigger than differences between same sector from distinct lists (Tables IX, XI, XIII and XV).

I. Correlation of measures
Correlation of degree (how many participants the participant related to) and strength (how many interactions) measures is substantially smaller for intermediary sector. This raises interesting inquiries, to which the reader is invited, along with further analysis of Tables XVI, XVII and XVIII, as detailing their interpretation goes beyond the scope of this article. Noteworthy is the negative correlation of degree and message size (number of characters, tokens or sentences) that intermediaries presented.

J. Formation of principal components
Principal components formation seem to be the less stable of all features considered. First component, with ≈ 25% of dispersion, relies heavily on POS tags, and slightly on sizes of tokens, sentences and messages. Second component, with almost 12% of dispersion, blends topology, POS tags and size measures. Third component, with about 8.5% is based on nouns frequency and size measures. Fourth and fifth components present less than 5% of total dispersion, but are included for completeness of exposition.
Tables XIX-XXIII exhibit these and further insights.

K. Results still to be interpreted
These networks yield diverse characteristics, some of which were not of core importance for this step of the research. Even so, at least one of these characteristics was found interesting enough to be considered a result and an example of interesting artifacts found.
Histogram differences of incident and existent word sizes were found constant. That is, in each list, when a histogram of word sizes were made with all words written, and another histogram made with sizes of all different words, the cumulative positive difference of the two histograms were found constant for all lists analysed. When all known English words were considered, the difference was always ≈ 1.0. When stopwords were discarded, the difference found was different, but still constant, slightly above 0.5. When only stopwords were considered, the difference was ≈ 0.6. When only known English words that does not have wordnet synsets are used, this difference is ≈ 1.2.
These results currently lacks substantial interpretation, which is provocative and should lead at least to a research note. Appendix C and Figures 2-6 are dedicated to this histogram differences.

V. FINAL REMARKS
Human interaction networks yield diverse linguistic peculiarities reported by its members. This is a first systematic exploration of such peculiarities with primitive connective sectors (hubs, intermediary and periphery) in mind, as far the author knows. Results were regarded as stronger than envisioned from start, which poses diverse and intriguing questions. This results, confluent with recent research and development, some by the current author, are of core importance for social technologies and transformations, such as collection and diffusion and information, resource recommendation in linked data contexts, and open processes of legal documents refinement 5,6,9,11,12 . All the data used is public, all scripts used are online (see Appendix D).

A. Further work
Results suggests that less connected participants bring external content and concepts, while hubs qualify the content. This hold mainly as periphery uses more nouns while hubs present more adjectives. This should be further verified, maybe with a dedicated article.
Similarity measures of texts in message-response threads has been thought about by the author, and some results are being organized. These are two hypothesis obtained from recent experiments: • existence of information "ducts", observable through similarity measures. These might coincide with asymmetries of edges between vertexes pairs, with homophily or with message-response threads, to point just a few possibilities.
• Valuable insights can be driven from self-similarity of messages by same authors, of messages sent at the same period of the day, etc. This includes incidences of word sizes, incidences of tags and morphosintactic classes, incidences of particular wordnet synset characteristics and wordnet word distances.
Given current results, diversity and self-similarity should vary with respect to connectivity. Literature usually assumes that periphery holds greater diversity 10 , which should be further verified.
Other directions for next steps are: • Word sets are very useful to derive and confirm hypothesis. As an example, one can observe most incident or most basic words and word types in the English language, curses or words related to food.
• Interpretation of various unveiled results, such as the one exposed in Appendix C, and Figures 2, 3, 4, 5 and 6.
• Extend word class observations to include plurals, gender, common prefixes and suffixes, etc.
• Date and time should also be addressed in textual production of interaction networks, as potentially linked to participation habits and purposes (e.g. low dispersion of sent time). This was tackled by the author for the topological characterization of interaction networks 11 , but left aside in this article.
• Balance token diversity with corpus size, as pointed in section IV C.
• The textual features distributions are likely to be have more than one peak or other non-trivial characteristic. Therefore, further analysis should be made for comparing measures of interest.
• Extend analysis to the windowed approach used in the article where hub, peripheral and intermediary sectors where topologically characterized 11 .
• For ELE list, the more connected the sector, the longer the messages are. This is the inverse of what was found in the other lists, and was considered a peculiarity of the culture bonded with the political subject of ELE list, to be further verified.
• Tackle Portuguese analysis of interaction networks, as this research have ongoing implications in Brazil 12 .
• Analyse other lists.
• Analyse interaction networks from other platforms such as Twitter, Facebook, LinkedIn, Diaspora, etc.
• Emotion classification has not been done and was considered out of the scope for this stage of development, but should be addressed in a near future.
Wordnet synsets incidences was studied as well, as a potentially useful way to characterize networks and sectors. Core aspects understood as useful for this research include: • Incidence of hypernyms, hyponyms, holonyms and meronyms.
• Use and development of similarity measures of words, phrases and messages, by use of semantic criteria (Wordnet) and bag of words.

VI. ACKNOWLEDGMENTS
Renato Fabbri is grateful to CNPq (processo 140860/2013-4, project 870336/1997-5); Postgraduate Committee of the IFSC/USP; Prof. Dr. Leonardo Paulo Maia for the insights which lead to the adaptation of the Kolmogorov-Smirnov test presented in Appendix A; Appendix A: Adaptation of the Kolmogotov-Smirnov test

Canonical Kolmogorov-Smirnov test
Be F 1,n and F 2,n two empirical distribution functions, where n and n are the number of observations on each sample. The two-sample Kolmogorov-Smirnov test rejects the null hypothesis if: where D n,n = sup x [F 1,n − F 2,n ] and c(α) is given for each level of α:

Adaptation
We need to compare empirical distribution functions, so D n,n is given, as are n and n . Therefore, as all terms in equation A1 are positive and c(α) can be isolated: Tables VIII-XV are populated with values for c (α). When c (α) is high, low values of α are possible for the test to reject the null hypothesis. Therefore, when c (α) is greater than ≈ 1.7, it is reasonable to assume that F 1,n and F 2,n differ. • CPP, the development list of the standard C++ library 1 . Dominated by specialized computer programmers.
• ELE: list for discussion of the election reform 4 . Table I has an overview of these lists, in terms of participants, threads and messages in each of the primitive connective sectors.
2. Meaning of achronims and abbreviations in the following tables symbol meaning |x| the number of times x was found kw known word |x = | number of different x found kwss known word with (wordnet) synset kwsw known word that is a stopword ukwsw unknown word that is a stopword nsssw word without (wordnet) synset that is a stopword Other symbols are explained on the tables itself. Some concepts, such as contractions, token and char are standard in natural language processing, and the reader is invited to visit 8 .                     FIG. 2. Size of words that are known in English. Crossing of incident and existential sizes is around 5 (Figure 3 shows a shift to length 6-7 when consider only non stopwords). Words with three letters have maximum incidence, while most words have 7 letters. See subsection IV K for discussion and directions.
FIG. 3. Size of words that are known in English and are not stopwords. Crossing of incident and existential sizes is around 6-7 (figure 2 shows a shift to length 5 when considered stopwords). In this case, words with 4 letters have maximum incidence, while most words still have 7 letters. Exception for ELE, which exhibits maximum incidence of words with 5 letters and most words having 8 letters, which might be associated with ELE network typology discussed in tables III and . See subsection IV K for discussion and directions. FIG. 6. Size histogram of known English words that are not stopwords and do not return synsets. Differences in distribution suggests less stable behavior, with high incidence of few words high number of existing words with many letters. Observe difference ≥ 1, as observed only with all known words, but even higher. See subsection IV K for discussion and directions.