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DOI: http://dx.doi.org/10.1145/2818048.2820019

# #Bieber + #Blast = #BieberBlast:
Early Prediction of Popular Hashtag Compounds

Suman Kalyan Maity

Ritvik Saraf

Animesh Mukherjee

Dept. of CSE  IIT Kharagpur, India  Dept. of Maths & Computing  IIT Guwahati,
India  Dept. of CSE  IIT Kharagpur, India

###### Abstract

Compounding of natural language units is a very common phenomena. In this
paper, we show, for the first time, that Twitter hashtags which, could be
considered as correlates of such linguistic units, undergo compounding. We
identify reasons for this compounding and propose a prediction model that can
identify with 77.07% accuracy if a pair of hashtags compounding in the near
future (i.e., 2 months after compounding) shall become popular. At longer
times $T$ = 6, 10 months the accuracies are 77.52% and 79.13% respectively.
This technique has strong implications to trending hashtag recommendation
since newly formed hashtag compounds can be recommended early, even before the
compounding has taken place. Further, humans can predict compounds with an
overall accuracy of only 48.7% (treated as baseline). Notably, while humans
can discriminate the relatively easier cases, the automatic framework is
successful in classifying the relatively harder cases.

###### keywords:

hashtag compounds; popular hashtags; unpopular hashtags; popularity prediction

###### category:

H.4 Information Systems Applications Miscellaneous

###### category:

J.4 Computer Applications Social and Behavioral Sciences

##  1 Introduction

Hashtag is the new “paralanguage” of Twitter. What started as a way for people
to connect with others and to organize similar tweets together, propagate
ideas, promote specific people or topics has now grown into a language of its
own. As hashtags are created by people on their own, any new event or topic
can be referred to by a variety of hashtags. This linguistic innovation in the
form of hashtags is a very special feature of Twitter which has become
immensely popular and are also widely adopted in various other social media
like Facebook, Google+ etc. and have been studied extensively by researchers
to analyze the competition dynamics, the adoption rate and popularity scores.
However, there are very few attempts to study the linguistic aspects of
hashtag evolution over large time scales. One of the interesting and prevalent
linguistic phenomena in today’s world of brief expressions, chats etc. is
hashtag compounding where new hashtags are formed through combination of two
or more hashtags together with the form of the individual hashtags remaining
intact. For example, #PeoplesChoice and #Awards together form
#PeoplesChoiceAwards. #KellyRipa and #CelebrationMonth make
#KellyRipaCelebrationMonth; #WikipediaBlackout is formed from #Wikipedia and
#Blackout; #OregonBelieveMovieMeetup is formed from #Oregon, #BelieveMovie and
#Meetup; #Educational, #Ipad, #Apps together make #EducationalIpadApps etc. In
this paper, we identify for the first time that while some of these compounds
gain a high frequency of usage over time (even higher than the individual
constituents) many of them are soon lost into oblivion. We focus and
investigate in detail the reasons behind the above observations.

### Motivations

In etymology, we come across a very similar phenomenon where words are formed
from various other words sampled from the same or a different language.
Lexical compounding has been prevalent all through over the history of
evolution of any language [4, 25, 23]. For example, in English, ‘wheelchair’
has been formed from ‘wheel’ and ‘chair’, bookworm is the combination of
‘book’ and ‘worm’ with the meaning of the words getting completely modified
due to compounding. Similarly, ‘in so far’ has become ‘insofar’ with no
meaning getting altered. However, such compounding phenomena in social media
are far more prevalent than in standard texts and language.

Innovation and adoption are both important processes in language change [15,
45]. While innovation refers to the creation of new linguistic units, adoption
refers to its proliferation among wider groups of speakers. An innovative form
must be adopted by a significant number of speakers in order for observable
change to take place [45]. Hashtag is a linguistic innovation in social media.
Predicting the propagation and spread of hashtags in online communities is an
important aspect from both commercial and psychological perspectives. Not all
hashtags become popular, some of them become popular while most of them fall
into oblivion. There are numerous factors that drive hashtag popularity and
this popularity aspect have been studied extensively by various researchers
[31, 32, 38, 40, 41, 59, 63, 64, 67]. A significant proportion of the hashtags
used in social media are compound hashtags. In this paper, we attempt to early
predict whether a hashtag compound shall gain a higher usage frequency
(popularity) than the individual constituent hashtags forming the compound.
Note that our objective does not include identifying if two or more hashtags
are going to compound in future; instead, we are interested to automatically
identify cases where the popularity of an already formed compound is far more
than the individual components.

#### Compounds in Practice

Like general hashtags, predicting popular hashtag compounding is also an
important and interesting task. There are marketing strategic needs, needs for
fulfilling communicative intents (affective expression, political persuasion,
humor etc.) as well as spontaneous needs for use of hashtag compounds. For
example, the e-commerce company Amazon used #AmazonPrimeDay to promote the
discounted sale of its product. The hashtag is a compound of #Amazon and
#PrimeDay whereas the individual hashtag #PrimeDay was also popular. So, there
is a trade-off whether to use hashtag compounds or the uncompounded
constituents. Similarly, assume another scenario where an event is taking
place, say the premiere of a movie ‘The Imitation Game’. Here one can use both
the hashtags #TheImitationGame and #Premiere or can use a hashtag compound
#TheImitationGamePremiere. In this context, one needs to identify which
version one should use so that the hashtag being used gains a higher frequency
of usage in the near future. #CSCW2016 is being used to tag the activities
taking place related to the 2016 CSCW conference. This is also a compound
hashtag made of #CSCW and #2016 where #CSCW refers to all CSCW conferences and
#2016 refers to all the events/activities going to take place in 2016. The
hashtag #CSCW2016 is used for a more focused purpose and refering to only the
2016 edition of the conference whereas #CSCW could also have served the
purpose. Hashtag compounds also serve the communicative intents like political
campaign hashtags (#PresidentTrump = #President + #Trump : hashtag that shows
support for Donald Trump for the 2016 US Presidential election). Hashtag
compounding also happen spontaneously. These hashtags are generally
conversational or personal themed hashtags like #TheBestFeelingInARelationship
(#TheBestFeeling + #InARelationship), #ThrowbackThursday (#Throwback +
#Thursday), #ComeOnNowDontLie (#ComeOnNow + #DontLie).

Our prediction framework is different from existing popularity
prediction/trend identification algorithms/frameworks in the following ways.
The popularity prediction frameworks deal with the problem of predicting
whether a hashtag will become popular or not among a competing hashtag pool
consisting of all hashtags across various topics from the data stream and
filtered by the time window in which the prediction is being made. However, in
our framework, we want to predict whether the hashtag compound or the
individual constituent hashtags become popular. Therefore, our competition
space is smaller and topically more well-defined. This is simply to say
whether to adopt the compounded hashtags or not.

### Research objectives and contributions

In this paper, we study the hashtag compounding phenomena as a linguistic
innovation and investigate in detail the socio-linguistic reasons for its
adoption. Towards this objective, we make the following contributions in the
paper:

  * •

We study the hashtag compounding phenomena for the first time and put forward
various socio-linguistic reasons for the adoption (popularity) of the compound
hashtags

  * •

We conduct a thorough experiment with human subjects to identify how well
humans can predict popular hashtag compounds; the accuracy obtained is 48.7%
and constitutes the baseline.

  * •

We finally use the socio-linguistic aspects as features in a model that is
able to predict popular future hashtag compounds ($T$ = 2 months) with an
overall accuracy of 77.07% which is $\sim$58% improvement over the baseline.
Note that our results have the potential to strongly impact the trending
hashtag recommendation application of Twitter since it is able to predict
hashtags (i.e., compounds) that will be popular in future even before the
hashtags are born (i.e., before the compounding has taken place).

  * •

We also perform long term predictions at $T$ = 6 and 10 months after
compounding and achieve 77.5% and 79.13% accuracy respectively.

  * •

Finally, we perform a thorough correspondence analysis of the prediction
outcomes from human evaluations and the automatic framework. We observe
striking differences between the outcomes; while human evaluators are usually
able to discriminate the relatively easier cases, the automatic framework is
very successful in distinguishing some of the harder cases. We argue that this
is a methodological novelty of this paper and can be adopted in future
experimental studies of similar type.

### Organization of the paper

The remainder of the paper is organized as follows. Section 2 is a concise
review of the state-of-the-art. In section 3, we describe the dataset briefly.
In section 4, we discuss about the adoption/popularity of hashtag compounds.
Section 5 investigates the different linguistic aspects responsible for
hashtag compounding. In section 6, we outline the baseline experiments based
on the human judgments. In section 7, we introduce the prediction model and
describe the features. In section 8, we evaluate the model and discuss the
discriminative power of the features. In section 9, we perform a detailed
correspondence analysis of the hashtags judged by human evaluators and by the
automatic prediction framework. In section 10, we discuss the implications of
the findings from our study. Finally, in section 11, we conclude and point to
future direction of research.

##  2 Related work

### Language use in social media

There have been considerable works that focus on the content and its
linguistic aspects in social media. Honeycutt and Herring [28] analyzed
conversational exchanges in Twitter focusing on mentions. Ritter et al. [51]
developed an unsupervised learning approach to identify conversational
structure from open-topic conversations. Danescu-Niculescu-Mizil et al. [17]
studied how people adopt linguistic styles while in conversation on Twitter.
Eisenstein et al. [20] studied the role of geography and demographics on the
language in Twitter. Hong et al. [29] investigated the cultural differences in
Twitter’s language. Hu et al. [30] studied the characterization of linguistic
and psycholinguistic aspects in Twitter. Wang et al. [62] studied how people
curse each other in Twitter. Almuhimedi et al. [1] performed a large scale
quantitative analysis on deleted tweets.

There have been several studies on how language is used in social communities.
Kramer et al. [33] characterized different types of discourse in online
support groups (specifically, emotion writing, talkative, bipolar chat) for
successful communities. Arguello et al. [3] assessed whether members were
likely to post again using linguistic features of their posts. Nguyen et al.
[46] proposed a novel approach to identify latent hyper-groups in social
communities based on users’ language use. Cassell and Tverky [11] described
how linguistic interaction patterns change over time. Matthews et al. [43]
characterized how online communities combine multiple social tools. Tausczik
and Pennebaker [57] study the motivation of people participation in Q&A sites
(MathOverflow) and found that building reputation is an important incentive.
Matthews et al. [42] studied the relationship between member satisfaction and
language use within content posted in workplace online communities. Tang et
al. [56] analyzed the difference in language usage of international Facebook
users recently migrated to the United States to selectively self-disclose to
their old (native) and new (English-speaking) social circles.

### Lexical Compounding

Lexical compounding constitutes an active area of research; there have been
few studies on lexical compounding in English [4, 25] and other languages like
Italian, French, German, Spanish, Chinese etc. [2, 23, 34, 48, 61].
Pustejovsky [49] provided one of the earliest explanation of the compounding
phenomena within a compound based on the qualia modification relations in the
semantic composition within a compound. A recent study by Lee et al. [34]
discusses the formation of noun-noun compounds found in Chinese as well as few
other languages like German, Spanish, Japanese and Italian. Word compounding
is often termed as a form of lexical change which may be caused due to social
pressure, ease of pronunciation. Hacken [58] showed how translations can be
used as heuristics to determine the concept of compounding. Noun-noun
compounding is the most popular form and most studies are biased on
restricting themselves to this form only. However, Bagasheva in citealex have
studied the characteristics of compound verbs in English and Bulgarian
language and claimed that verbs also compound to a significant extent.
Bagasheva also showed that though the basic types of compound verbs are of the
form verb-verb (blowdry, drinkdrive) and noun-verb (babysit, brainwash,
proofread), other forms like noun-noun (handcuff, stonewall), adjective-noun
(fastrack, badmouth), adjective-verb (whitewash, dryclean), preposition-
noun/preposition-verb (overrun, underestimate) are also legitimate in English.
We shall observe that similar kinds of POS (Parts of Speech) tag combinations
are also present in case of hashtag compounds. In principle, we have attempted
to merge the socio-linguistic features with information technological
research. All of the above studies in linguistics considered anecdotal
evidences by showcasing various examples and mostly attempted to study the
formation of compounds. The main challenge of this type of research was the
non-availability of temporal data of language change. We try to bridge the
existing gap by considering large-scale social media data and study the
compounding phenomena assuming hashtags as linguistic units. The previous
studies on lexical compounds as discussed above have been mostly about
understanding the formation of compounds whereas our analysis of the hashtag
compounds is focused on the adoption of the compounds. There are differences
in the mechanisms of compound formation in ordinary language and in social
media. While lexical compounding is mostly spontaneous in nature, there are
many purposes to which hashtag compounds are being put in social media. One
aspect of it is market strategic hashtags like #AmazonPrimeDay, #CSCW2016 etc.
Another reason of it is to fulfill communicative intents such as affective
expression, political persuasion, or humor; for example hashtags like
#YesAllWomen, #FeelTheBern, #BlackLivesMatter, #PresidentTrump etc. There is
also spontaneous pressures of compounding like #TheBestFeelingInARelationship,
#YouKnowItsRealWhen, #RelationshipTips etc.

Our work has been inspired by several studies [16, 68, 9, 21] that focused on
hashtags as linguistic units and attempted to identify the systematic
similarities/differences with standard natural languages entities. Cunha et
al. [16] studied hashtags as linguistic innovation and characterized the
formation and usage of Twitter hashtags. Zappavigna [68] explored how hashtags
enact three simultaneous communicative functions: marking experiential topics,
enacting interpersonal relationships, and organizing text. Caleffi [9]
analyzed hashtagging as a productive process of word formation in English and
Italian.

### Lexical Blending

Another closely related innovation phenomena is lexical blending where a word
is formed from two or more words fused into one another. For example, brunch
(breakfast + lunch), fantabulous (fantastic + fabulous), entertoyment
(entertainment + toy) etc. This linguistic form of word reduction has been
studied widely [8, 10, 12, 13, 14, 24, 36, 44, 50]. Gaskell and Marslen–Wilson
gaskell1999 have proposed a distributed model of speech perception for
identification of blends, ambiguity etc. in spoken language. Cook and
Stevenson cook2010 have proposed a statistical model for identifying the
lexical blend’s source words from the observed linguistic properties of the
blend. In a subsequent study, Cook cook12 has proposed a regular expression
based method for identifying lexical blends in social media.

### Hashtag popularity

Hashtags are a way for social media users to tag their posts with keywords,
which in turn helps in meaningfully organizing the posts to make the contents
on social networks easily searchable. Hashtags have various utilities. These
are used in social campaigns, political campaigns, marketing and so on. They
also provide great way to get people talking, and let them jump into
discussions. For example, #Polichat is a popular stream of conversation used
by political and digital professionals. Therefore, it is important to know the
popular and trending hashtags so that it is possible to filter out meaningful
contents from the streams of data. There have been many studies on hashtag
adoption (popularity) [31, 32, 38, 40, 41, 59, 63, 64, 67]. Tsur and Rappoport
in [59] performed content based prediction of hashtag popularity. Ma et al. in
[41] proposed a framework for predicting popularity of newly emergent
hashtags. They showed that the contextual features based on the underlying
social network of the users of the hashtag are more important than the content
based features in predicting the popularity of a hashtag on a daily basis.
Kamath and Caverlee [31] have modeled the geo-spatial propagation of online
information spread to identify which hashtags will become popular in specific
locations. Another notion of hashtag popularity is the “burstiness” of
hashtag, the phenomena which involes sudden rise in hashtag usage and quick
fall thereafter. Kong et al. [5, 32] studied the burstiness of hashtag on a
temporal scale.

While retweets and followers support a hashtag’s growth, they also
paradoxically undermine its persistence. Various researchers have tried to
systematically analyze the features that contribute to the growth and
stabilization of the hashtags. Yang and Scott [66] examined the roles of
“relevance” and “exposure” for hashtag adoption [66]. Yang et al. [67] studied
the duality of hashtags as topical identifiers and a symbol of community
membership. Lin et al. [38] studied the growth, survival, and context of novel
hashtags during the 2012 U.S. presidential debate. They proposed a framework
to capture dynamics of hashtags based on their topicality, interactivity,
diversity, and prominence.

### Adoption and propagation of topical information in Twitter

There have been several studies on the roles the users play in adoption and
propagation of topical information in Twitter. Lerman and Ghosh [35] studied
the user activities in Digg and Twitter. Lin et al. [37] studied the evolution
of a topic and revealed the diffusion path of the topic in the community. They
proposed a probabilistic model based on textual documents, social influences
of the users and topic evolution. Romero et al. [53] observed that different
topical category (sports, music, Idioms) of hashtags have different
propagation pattern. Gomez et al. [26] studied the information diffusion among
blogs and online news sources. Wu et al. [65] analyzed the “elite” users and
their roles in information spread. Starbird et al. [55] and Vieweg et al. [60]
addressed the characterization of events (natural hazards) by the Twitter
users who posted tweets among them. Shamma et al. [54] proposed metrics for
identification of nature of topics/ events (peaky or persistent) in Twitter
data stream. De Choudhury et al. [18] characterized Twitter users into three
primary categories: organizations, journalists/media bloggers, and ordinary
individuals. Bhattacharya et al. [6] characterized topical groups based on
their network structures and tweeting behaviors. They distinguish between two
types of users within a topical group; experts who are likely to be
authoritative sources of information on specific topics, and seekers who are
interested in gathering information on these topics.

Most of the above approaches in lexical compounding/blending propose
theories/hypotheses with anecdotal evidences from various languages. However,
we perform an in-depth large scale analysis of hashtag compounding phenomena
and the adoption characteristics of the hashtag compounds in social media from
publicly available data. We propose a prediction framework for early
prediction of the popular hashtag compounds that is manifolds better than the
baseline system based on human judgments.

##  3 Dataset description

Twitter provides $1\%$ random sample of all the tweets via its sample API in
real time. This API has been used to crawl tweets from $1^{st}$ July, 2011 to
$31^{st}$ December, 2013. For analysis, we consider the users who have
mentioned English as their language in their profile. We also performed a
second level filtering of the tweets by a language detection software [39] to
remove any non-English tweets from the dataset. The data are then tokenized
using the same tokenizer used by the CMU POS tagger [47]. In total, the
dataset consists of $\sim 1$ billion tweets.

##  4 Adoption of hashtag compounds

In this section, we discuss the phenomena of compounding and the adoption of
hashtag compounds in social media. For detection of the hashtag compound, we
consider 6 months data from $1^{st}$ January, 2012 to $30^{th}$ June, 2012.
For any hashtag of length $\geq$ 6 in this data, of the form #AB, we search
for #A and #B in the data from $1^{st}$ July, 2011 till the time point when
the first appearance of #AB is found. Note that, both #A and #B themselves
could be single words or compound words. For example, #HighSchoolMemories
(#HighSchool + #Memories), #NeverShouldYouEver (#NeverShould + #YouEver) etc.
We restrict our study to hashtag compounds that are formed by only two
constituent hashtags (i.e., ignore further divisions of the constituent
hashtags). To avoid ambiguity, we do not consider those compounds that can be
formed due to the compounding of multiple pairs of constituent hashtags. For
example, #IStillHaventStarted (#IStill + #HaventStarted or # IStillHavent +
#Started).

Note that not all hashtag compounds become popular after the compounding. Out
of $\sim 2$ million candidate compounds, only $2\%$ are found to attain a
frequency of usage more than both the constituent hashtags. In table 1, we
show some examples of the compounds which are more frequently used than the
constituent hashtags right from the point of the compound formation. In the
same table, we also present examples of some compounds that do not gain
frequency after the compound formation. Without loss of generality, we refer
to the first type of hashtag compound as “popular” and the second type as
“unpopular”. We study in detail these two types of compound hashtags and their
properties, thereby, identifying factors differentiating them. Understanding
the precise reasons for certain compounds becoming popular could have far
reaching impact both linguistically as well as in trending hashtag
recommendation service where recommendation of a “would-be-popular” compound
can be made even before such compounds are born.

Table 1: Few examples of the popular and unpopular compound hashtags. The
numbers in the parenthesis denote the frequency of the hashtag 10 months after
the merging took place.

Popular | Formation | Unpopular | Formation
---|---|---|---
#HighSchoolMemories (21700) | #HighSchool (395) + #Memories (4178) | #LoveOomf (1) | #Love (14525) + #Oomf (142299)
#FreshmanAdvice (9144) | #Freshman (102) + #Advice (124) | #OomfPussy (5) | #Oomf (142671) + #Pussy (11010)
#QuestionsIHateAnswering (4186) | #QuestionsIHate (14) + #Answering (1) | #ILovePorn (2) | #ILove (428) + #Porn (46715)
#OperationLegalizeWeed (3978) | #Operation (18) + #LegalizeWeed (12) | #YOLOForJesus (1) | #YOLO (47056) + #ForJesus (4)
#WikipediaBlackout (2638) | #Wikipedia (202) + #Blackout (524) | #HateCanada (3) | #Hate (1622) + #Canada (2399)
#GameInsight (2633) | #Game (689) + #Insight (49) | #SweetBabyJesusThatsGood (1) | #SweetBabyJesus (45) + #ThatsGood (27)
#CNNDebate (2615) | #CNN (1637) + #Debate (125) | #RegentStreet (1) | #Regent (2) + #Street (223)
#GoldenGlobes (2581) | #Golden (125) + #Globes (61) | #ComingBackBlack (2) | #ComingBack (12) + #Black (1205)
#GhettoSpellingBee (255) | #Ghetto (134) + #SpellingBee (8) | #LiquidationMonday (3) | #Liquidation (51) + #Monday (965)
#LilWaynesGreatestHits (254) | #LilWaynes (1) + #GreatestHits (132) | #MavericksNation (4) | #Mavericks (210) + #Nation (136)

### Popularity trend of the hashtag compounds

In this subsection, we shall discuss about the monthly popularity trend
(frequency of the hashtag in tweets) of the popular hashtag compounds in the
next 10 months after compounding. We categorize the popular hashtags into some
finer classes - a) the frequency of the compound is always higher than that of
its constituent ones b) the frequency of the compound is always higher
compared to its constituent ones except for one month c) the frequency of the
compound is always higher compared to its constituent ones except for two
months d) the class containing the rest of the popular hashtag compounds. For
the first three categories, the popularity trend suggests “winner-takes-all”.
We also observe that in many cases, though initially the frequency of the
hashtag compounds remains higher than its constituent hashtag, however as time
progresses the frequency falls below the constituent hashtags (see fig 1(d)).
On an average, we see that for time larger than 2 months from the time point
of compounding, this phenomenon takes place. This is the reason we select the
first (early) prediction time point $T$ = 2 months (see section 7 for
details).

Figure 1:  The popularity trend of various categories of popular hashtags. The
average frequency (no. of tweets) profile of the hashtags after the time of
compounding for the hashtag triplets (#A, #B and #AB) where the avg. frequency
of the compounded hashtags #AB are higher than both of the constituent
hashtags #A and #B a) in all months b) in all but one month c) in all but two
months d) in some months.

##  5 Linguistic aspects of hashtag compounding

This section is inspired by the observations made by researchers working on
various aspects of lexical compounding. In addition to this, we also identify
certain other interesting issues specific to hashtag compounding some of which
might be as well generalized to mainstream lexical compounding research. In
the rest of this section, we shall discuss various linguistic aspects of
hashtag compound formation. We shall mostly focus on the compounding zone
where the two constituent hashtags merge.

### Part-of-speech combination

In lexical compounding, we find evidences of various types of compounds based
on the POS [34] of the individual words that get compounded across various
languages. For example, noun-noun, verb-noun, noun-verb, verb-verb etc are
some common forms. We hypothesize that a similar phenomenon is instrumental in
case of hashtags also. To validate this hypothesis we POS tag the individual
hashtags using the CMU POS tagger [47], which is the state-of-the-art POS
tagger available for Twitter. For a compound of the form #AB which is made up
of #A and #B, we find POS of #A and POS of #B to determine various
combinations of POS-based compound formation. Note that the individual
hashtags #A and #B can themselves be also compounds like #ab and #cd where $a$
and $b$ are words compounding to #A and $c$ and $d$ are words compounding to
#B. In such scenarios, we consider the compounding zone and find the POS of
the last part of #A (i.e, $b$) and the POS of the first part of #B (i.e.,
$c$). In figure 2, we show the distribution of various POS-combinations of the
hashtag compounds. We observe that there is a clear distinction present
between the distribution of POS combinations for popular and unpopular
compounds. Most prominent POS combinations in case of popular compounds are
proper noun-proper noun, followed by common noun-common noun, determiner-
common noun and verb-determiner; however, the most prominent POS combinations
for the unpopular hashtag compounds are common noun-common noun, Adjective-
common noun, determiner-common noun, proper noun-proper noun etc. Among both
popular and unpopular compounds, the common noun-common noun pair seems to be
very prevalent.

Figure 2:  Distribution of top 20 most prominent combinations of part-of-
speech tags of a) popular b) unpopular compound hashtags. The full form of the
acronyms of the POS tags used here are as follows: ^ - Proper Noun, $N$ \-
Common Noun, $O$ \- Pronoun, $V$ \- Verb, $A$ \- Adjective, $R$ \- Adverb, $D$
\- Determiner, $P$ \- Pre or post position

### Named entity combination

Apart from the POS tags, we also perform named entity recognition of the
constituent hashtags forming the compound to understand which types of
entities merge. We use a named entity recognition tool [52] for identifying
named entities of the words in the hashtags forming the compounds. For a
compound hashtag (#AB = #A + #B where #A = #ab and #B = #cd), we find the
named entity of last word of #A and the named entity of first word of #B to
determine various combinations of named entity-based compounding. For other
cases where #A, #B are single words we perform the recognition directly on
these words. Figure 3 shows the distribution of the top 20 most prominent
named entity combinations for popular (fig 3(a)) as well as unpopular hashtag
compounds (fig 3(b)). Though in 85% cases we find that the constituent words
are non-entities, from the remaining 15% cases, we find various named entity
combinations and the distribution of these combinations are indeed very
different for the popular and the unpopular classes. In general, the most
prevalent named entity combinations where both the constituent hashtags denote
entities are (B-person I-person) followed by (B-product I-product) and
(B-movie I-movie). However, the fraction of each such pair for the popular
compounds is very different for the unpopular ones.

Figure 3:  Distribution of top 20 most prominent combinations of named
entities of a) popular b) unpopular compound hashtags. For detailed
description of the named entity types, refer to
https://github.com/aritter/twitter_nlp

### Out-of-vocabulary / In-vocabulary combination

With the advent of new words/slangs in social media, there is an increasing
trend of usage of out-of-vocabulary (OOV) words [19]. Motivated by above
observation, we study if OOV words have a role in compound formation. We use
GNU Aspell dictionary to determine whether a given word is an OOV or INV (In-
Vocabulary). As stated earlier, for each compound hashtag of the form #AB
formed by #A and #B, we find the nature of the ending word of #A and the
beginning word of #B. In table 2, we report the distribution of various
combinations for both the popular and unpopular compounds. The most prevalent
combination in both cases are the merge of two INV words (though varying in
the percentages, $\sim 44\%$ in case of popular compounds compared to $\sim
67\%$ in case of unpopular compounds). There is also a marked distinction in
the rank order in which the combinations are used apart from usage
variability. The rank order in case of popular compounds is OOV-OOV, INV-OOV,
OOV-INV whereas for the unpopular ones it is OOV-INV, INV-OOV, OOV-OOV.

Table 2: Distribution of various combinations of OOV and INV words of popular and unpopular compound hashtags. Popular | Unpopular
---|---
Combinations |  % |  Combinations |  %
INV-INV |  43.9 |  INV-INV |  66.9
OOV-OOV |  20.7 |  OOV-INV |  14.0
INV-OOV |  19.8 |  INV-OOV |  13.6
OOV-OOV |  15.6 |  OOV-OOV |  5.5

##  6 Baseline system

The main purpose of the human prediction is to find out whether humans can
identify the popular compound just from the structural information of the
hashtags. If humans can easily identify popular compounds then the whole
problem of predicting popular hashtag compounds is not interesting. The
purpose of this work is to design an automated framework which will assist
humans adopting hashtag compounds that are going to be popular in near future
and we shall compare how good this system performs by considering human
judgment as baseline. To understand whether humans can predict if a compound
is going to be popular in future, we conduct an online
survey111http://bit.ly/1ARJlRp among $72$ agreed participants (students,
researchers, professors, technical persons) with ages ranging between 18-34
years. We choose 600 hashtag compounds randomly from the set of 2000 compounds
used for classification (see section 8). Each participant is given a set of 25
questions. In each question, the participants are given the hashtag compound
as well as the constituent hashtags and are asked whether the compound hashtag
would become more popular in future than both the constituent hashtags. If
they are not sure of the answer, they have the option to indicate that they do
not know. Each question is asked to exactly 3 participants. A detailed
analysis of the survey results is outlined below. We receive $\sim 15\%$
responses where the participants indicated that they are unsure. Out of
remaining 85% responses, 53.3% responses are found to be correct answers. We
adopt majority voting technique to evaluate each question. $\sim 10.5\%$ of
the questions remain undecided due to all the possible answers getting equal
number of votes. Out of the remaining questions, we obtain an accuracy of
54.5% and an overall accuracy of $48.7\%$. We also perform averaging of
responses. For each hashtag compound, we find the fraction of responses in
agreement with the real data. Then, we take average for all the hashtag
compounds. This yields an overall accuracy of $45.33\%$. To find out the
inter-evaluator agreement, we compute Fleiss’ Kappa [22] which is found to be
0.15. In order to identify how the individual user judgments are, we compute
response accuracies for each user separately. The median user response
accuracy comes out to be 44% and the standard deviation of the user response
accuracies is 0.12. The maximum and minimum user response accuracies are 68%
and 12% respectively.

Table 3: Baseline accuracies. Method | Overall Accuracy
---|---
Majority Voting | 48.7%
Averaging | 45.33%

From the above observations (table 3) and the discussions, we can conclude
that human judgment is on an average poor. This motivates us to develop an
automated prediction framework which as we shall see is highly accurate. We
consider the human judgment accuracies as a baseline for our prediction model
presented in the next section.

##  7 Prediction model

In this section, we propose a model for early prediction of the “would-be
trending” hashtag compounds. For the prediction task, we observe the
constituent hashtags (#A, #B) for $t$ = 6 months before they get compounded
together to form #AB and predict whether #AB will be more popular (in terms of
frequency in tweets) than both #A and #B or not after $T$ months from the time
point of the compounding (see fig 4). In our setting, we consider $T$ = 2, 6
and 10 months.

Figure 4: A schematic of our proposed framework. Here the popular category
refers to those cases where frequency of usage of #AB $>$ frequency of usage
of #A AND frequency of usage of #B. The rest of cases are categorized as
unpopular.

For the task of prediction, we learn three major types of features :
Hashtag content features \- the features that are related to the content of
the hashtag only
Tweet content features \- the features that are related to the tweets in which
the hashtags appear
User features \- these include various properties of the users who tweet the
hashtags, such as their social influence etc.

### Hashtag content features:

For each hashtag we extract various attributes related to its content. These
are mostly the attributes related to characters, words and the nature of the
words that are used in the hashtag.

#### Character length of the compound hashtag

Due to the 140 character-limit on the tweets, character usage is vital and is
hence a constraint on the size of the hashtags too. People tend to express
their feeling using smaller number of characters but there is a trade-off;
smaller sized hashtags do not always serve their purpose. Therefore, the
number of characters in the hashtag compound is a feature of the model.

#### Number of words in the compound hashtag

The number of the words in a compound hashtag is also important because more
words may mean more expressibility. The compound hashtag might be more
expressible than the constituent hashtags.

#### Presence of n-grams in English texts

We segment the words in the compound hashtag and search for 2, 3, 4, 5 grams
of the constituent words in the corpus of 1 million contemporary American
English words222http://www.ngrams.info/samples_coca1.asp. We use presence of
any of these n-grams as a feature for the classifier.

#### Part-of-speech diversity of the words in the compound

We use standard CMU POS tagger [47] as stated earlier for identifying the POS
tags after segmenting the compound hashtag into its constituent words. We
define the POS diversity (POSDiv) as follows:

$POSDiv(h_{i})=-\sum_{j\in P}p_{j}\times\log(p_{j})$

where $h_{i}$ is the $i^{th}$ hashtag and $p_{j}$ is the probability that a
word is labeled by the $j^{th}$ POS from the set $P$ of all possible POS tags.
We use this diversity metric as a feature for our classifier.

#### Part-of-speech combination

As observed earlier in section 5, there is clear distinction in the
distribution of POS tag combination for the popular and the unpopular
compounds in the compounding zone. Motivated by this observation, we consider
the POS tag combinations as features to the classifier. We consider 20 such
most prevalent POS tag combination, each of them acting as a binary feature.

#### Named entity combination

Similar to the above feature, we also consider the named entity combination as
an important feature for the classifier. In section 5, we observe that the
most prominent named entity combinations are different for the popular and the
unpopular hashtag compounds. Therefore, we consider 20 most prominent named
entity combinations, each one of them as a binary feature for the classifier.

#### OOV/INV combination

We also observe in section 5 that there are significant differences in the
distribution of the various INV/OOV combination for the words at the point of
merge. To utilize this striking difference, we consider all four combinations
(OOV-OOV, INV-OOV, OOV-INV, INV-INV) as 4 individual binary features for the
classifier.

### Tweet content features:

The content of tweets that use a hashtag is also a significant determinant of
the popularity of a hashtag compound. In this subsection, we shall be
describing a series of tweet content features.

#### Word overlap

We compute the overlap coefficient333overlap coefficient between two sets $A$
and $B$ is given by $overlap(A,B)=\frac{|A\cap B|}{min(|A|,|B|)}$ between the
set of words appearing in tweets with #A and #B. This overlap coefficient act
as a feature for our classifier.

#### n-gram overlap

For the compounding hashtags #A and #B, we consider the words appearing in
tweets with those hashtags separately and search for 2, 3, 4, 5 grams in the
corpus of 1 million contemporary American English
words444http://www.ngrams.info/samples_coca1.asp. We then find out the overlap
coefficient between the set of valid n-grams for #A and #B.

#### Average frequency of the overlapping set of n-grams

In a similar way as above, we find out the average frequency of the n-grams in
the contemporary American English corpus for the overlapping set of n-grams
found from the set of tweets for #A and #B.

#### Collocation frequency of the compounding pair

To understand whether collocation of the compounding hashtags in tweets has
effect on the compound formation thereafter, we consider the collocation
frequency of the compounding hashtags as feature for the classification task.

#### Clarity of the compounding hashtags

Hashtag clarity, a metric that has been defined in [40] quantifies topical
cohesiveness of all the tweets in which the hashtag appears. Clarity of
hashtag $i$ ($HashClarity(i)$) is computed as the Kullback-Leibler (KL)
divergence between the unigram language model inferred from the document
$D_{i}$ containing all the tweets for the $i^{th}$ hashtag and the background
language model from the entire tweet collection $T$. If a hashtag refers to a
specific topic, then the high probabilities of a few topic-relevant words
distinguish its tweets from the background.

$HashClarity(i)=-\sum_{w\in
D_{i}}p(w|D_{i})\times\log\frac{p(w|D_{i})}{p(w|T)}$

We compute hashtag clarity for both the compounding hashtags #A and #B and use
them individually as features.

#### Word diversity of the compounding hashtags

This feature tells us how much diverse are the words related to a hashtag. If
$D_{i}$ is the document containing all the tweets in which hashtag $i$ appears
and $p(w|D_{i})$ is the probability of a word belonging to the document
$D_{i}$ then word diversity of hashtag $i$ is defined as follows

$WordDiv(i)=-\sum_{w\in D_{i}}p(w|D_{i})\times\log p(w|D_{i})$

We compute word diversity of both the compounding hashtags #A and #B and use
each of them as a feature.

#### Avg. topic overlap among the compounding hashtags

Topical overlap is an important aspect for a hashtag compounding phenomena.
More the constituent hashtags are aligned to the similar topics, more is the
chance that the hashtag compound becomes popular. For topic discovery from the
tweet corpus, we adopt Latent Dirichlet Allocation (LDA) [7] model, a renowned
generative probabilistic model for discovery of latent topics. For a hashtag
$i$, we consider all the tweets in which the hashtag appears as a document for
the LDA model. Now, considering all the hashtags we experiment with, we have a
collection of documents on which we run LDA to obtain the word distribution
across all the topics for each document. Next, for each of the topic, we find
top 100 words according to the belongingness probability of the words in the
topic for both #A and #B. We then compute the overlap between these two sets.
For each topic, we compute topic overlap (in terms of the number of common
words belonging to that topic) between #A and #B and consider the average of
them as a feature for the classification model.

### User features:

Users play an important role in hashtag adoption. People use/adopt hashtags
according to their personal interest, their social influence etc. In this
subsection, we shall discuss a set of user features which could be important
for discriminating a popular compound from an unpopular one.

#### Unique and common users

We hypothesize that the extent of adoption of the constituent hashtags could
be an important indicator of the overall popularity of the compound formed.
For this reason, we measure the number of unique users tweeting using either
of the constituent #A or #B. These two counts act as classification features.
In addition, we also identify the number of common users who tweet both the
constituent hashtags #A and #B either in the same tweet or in different
tweets. This is another feature for the classification model.

#### Mention behavior of the users

People tend to mention people in tweets whom they like to engage in
conversations. Thus, mention behavior in tweets for constituent hashtags might
affect adoption of the hashtag compound. Therefore, we find the number of
unique users mentioned in tweets containing the constituent hashtag #A. The
same is found for #B. These two act as features for the classifier. We further
find the number of common users being mentioned either in the same or
different tweets containing both the constituent hashtags #A and #B. This is
another feature for the classification model.

#### Retweet behavior of the users

Retweeting is an inherent indicator of increasing popularity of a hashtag.
More retweets usually mean more popularity. Similar to the case of mentions,
we find the number of unique retweets for the set of tweets containing the
constituent hashtag #A. The same goes for #B. These two act as features for
the classification model. We thereafter find number of common retweets using
both the compounding hashtags #A and #B. This is also a classification
feature.

##  8 Performance evaluation

In this section, we analyze the performance of our prediction model. For
prediction task, we use 2000 hashtag compounds (#AB) whose constituent
hashtags #A and #B have each occurred in at least 50 tweets six months before
the time point at which the compounding took place. We use Support Vector
Machine (SVM) and logistic regression classifiers available in Weka Toolkit
[27] for classifying the data into popular and unpopular hashtag compounds. We
perform 10-fold cross-validation as well as training and testing on separate
dataset by splitting the data into 9:1 (see table 4 for details). We achieve
77.07% accuracy with high precision and recall rates while predicting after
$T$ = 2 months. As one increases this time period, the accuracy of prediction
increases, although not very significantly. For long-term predictions after
$T$ = 6 months and 10 months, the accuracy obtained are 77.5% and 79.13%
respectively. Both the classifiers yield very similar classification
performance; however the logistic regression model gives better area under the
ROC curve compared to SVM. We also observe that the number of topics ($K$) of
LDA do not have a significant effect on the classification results. For
$K=30$, we achieve the best accuracy and the area under the ROC curve. We
observe that our prediction accuracy improves by $\sim$58% over the baseline
accuracy produced by human judgment.

Table 4: Performance of various classifiers at different time of prediction ($T$ = 2, 6, 10 months) for different topic selection for LDA feature with number of topics ($K$ = 10, 20, 30, 40, 50). The classification results are shown for 10-fold cross validation as well as with separate training and testing set in 9:1 ratio. Time period |  Classifier |  $K$ |  Accur-acy |  Preci-sion |  Recall |  F-Score |  ROC Area
---|---|---|---|---|---|---|---
$T$ = 2 months |  SVM(10-fold cross validation) |  10 |  76.18 |  0.762 |  0.762 |  0.762 |  0.762
20 |  76.42 |  0.764 |  0.764 |  0.764 |  0.764
30 |  77.07 |  0.771 |  0.771 |  0.771 |  0.771
40 |  76.37 |  0.764 |  0.764 |  0.764 |  0.764
50 |  76.72 |  0.767 |  0.767 |  0.767 |  0.767
Logistic Regression(10-fold cross validation) |  10 |  76.13 |  0.761 |  0.761 |  0.761 |  0.836
20 |  76.43 |  0.764 |  0.764 |  0.764 |  0.839
30 |  76.48 |  0.765 |  0.765 |  0.765 |  0.841
40 |  76.27 |  0.763 |  0.763 |  0.763 |  0.838
50 |  76.42 |  0.764 |  0.764 |  0.764 |  0.837
SVM(seperate train and test set) |  30 |  77.7 |  0.777 |  0.77 |  0.772 |  0.771
Logistic Regression(seperate train and test set) |  30 |  77.5 |  0.781 |  0.775 |  0.776 |  0.834
$T$ = 6 months |  SVM(10-fold cross validation) |  10 |  76.85 |  0.769 |  0.768 |  0.768 |  0.768
20 |  77.07 |  0.771 |  0.771 |  0.771 |  0.771
30 |  77.52 |  0.775 |  0.775 |  0.775 |  0.775
40 |  77.18 |  0.772 |  0.772 |  0.772 |  0.772
50 |  76.4 |  0.764 |  0.764 |  0.764 |  0.764
Logistic Regression(10-fold cross validation) |  10 |  75.84 |  0.758 |  0.758 |  0.758 |  0.817
20 |  75.95 |  0.76 |  0.76 |  0.759 |  0.821
30 |  76.62 |  0.766 |  0.766 |  0.766 |  0.823
40 |  76.17 |  0.762 |  0.762 |  0.762 |  0.82
50 |  75.84 |  0.758 |  0.758 |  0.758 |  0.819
SVM(seperate train and test set) |  30 |  80 |  0.832 |  0.8 |  0.802 |  0.819
Logistic Regression(seperate train and test set) |  30 |  78.89 |  0.817 |  0.789 |  0.791 |  0.888
$T$ = 10 months |  SVM(10-fold cross validation) |  10 |  76.7 |  0.768 |  0.767 |  0.767 |  0.767
20 |  78.48 |  0.786 |  0.785 |  0.785 |  0.785
30 |  79.13 |  0.792 |  0.791 |  0.791 |  0.791
40 |  77.83 |  0.78 |  0.778 |  0.778 |  0.778
50 |  77.02 |  0.772 |  0.77 |  0.77 |  0.77
Logistic Regression(10-fold cross validation) |  10 |  77.7 |  0.777 |  0.777 |  0.777 |  0.824
20 |  78.31 |  0.784 |  0.783 |  0.783 |  0.827
30 |  78.65 |  0.787 |  0.787 |  0.786 |  0.833
40 |  77.99 |  0.781 |  0.78 |  0.78 |  0.828
50 |  78.6 |  0.786 |  0.786 |  0.786 |  0.827
SVM(seperate train and test set) |  30 |  79.03 |  0.79 |  0.825 |  0.79 |  0.791
Logistic Regression(seperate train and test set) |  30 |  77.42 |  0.816 |  0.774 |  0.774 |  0.892

### Ablation experiments for feature importance

To understand the importance of the features, we perform ablation experiments
by removal of various feature types. In table 5, we present the contribution
of different combinations of feature types, demonstrating how each of these
feature types affect the classification and whether any feature type is masked
by a stronger signal produced by other feature types. We observe that tweet
content features are the most discriminative ones whereas hashtag content
features are the least. However, the combination of tweet content features
with user features and tweet content features with hashtag content features
yield very similar accuracy values.

Table 5: Performance of various combinations of feature categories for $K=30$ and time period of observation $t$ = 2 months. Feature model |  Accuracy
---|---
All |  77.07%
tweet content + user |  75.9%
tweet content + hashtag content |  75.12%
hashtag content + user |  72.4%
tweet content |  74.1%
user |  68.18 %
hashtag content |  65.04%

### Discriminative features

In this subsection, we discuss about the discriminative power of the
individual features. In order to determine the discriminative power of each
feature, we compute the chi-square ($\chi^{2}$) value and the information
gain. Table 6 shows the order of all features based on the $\chi^{2}$ value,
where larger the value, higher is the discriminative power. The ranks of the
features are very similar when ranked by information gain (Kullback-Leibler
divergence). Among tweet content features, the most discriminative features
are the overlap features like n-gram overlap, word overlap. In addition, we
observe that the hashtag features like the INV/OOV combination, POS
combinations, are also highly discriminative.

Table 6: Top 30 predictive features and their discriminative power for $K=30$.

Rank |  $\chi^{2}$ Value | Feature
---|---|---
1 |  476.49 | n-gram overlap
2 |  460.34 | Avg. frequency of common n-grams
3 |  420.99 | Word overlap
4 |  314.13 | no. of unique retweets with #A
5 |  285.79 | no. of unique users tweeting #A
6 |  281.71 | no. of unique retweets with #B
7 |  275.42 | no. of unique users tweeting #B
8 |  273.04 | Word diversity of #A
9 |  252.92 | Hashtag clarity of #A
10 |  222.32 | Word diversity of #B
11 |  213.82 | no. of unique users mentioned in #A
12 |  208.41 | no. of unique users mentioned in #B
13 |  204.14 | Hashtag clarity of #B
14 |  152.38 | INV-INV
15 |  136.74 | OOV-OOV
16 |  111.69 | POS diversity
17 |  105.52 | no. of common users tweeting using #A and #B
18 |  101.47 | Avg. topic overlap
19 |  86.39 | total no of words in #AB
20 |  84.20 | no. of common retweets for #A and #B
21 |  70.08 | AN
22 |  65.22 | OO
23 |  46.13 | ^^
24 |  39.99 | no. of characters in #AB
25 |  25.42 | no. of common users mentioned for #A and #B
26 |  23.2 | NN
27 |  16.13 | B-personI-person
28 |  13.55 | P^
29 |  9.51 | OOV-INV
30 |  9.04 | AA

##  9 Correspondence analysis

In this section, we shall compare the outcomes of the automatic prediction
framework with the human judgment results. We consider this correspondence
analysis to be a methodological novelty of our work and argue that such a
study can form a crucial part of any future research of similar type.

For this purpose, we select from among the set of 2000 hashtags those 600
cases that have been used for the human judgment experiments. This time we
train the model on the 1400 (i.e, 2000 - 600) cases and test on the 600 cases
($T$ = 6 months). We then compare the predicted labels from the automatic
prediction framework and the human judgment labels decided via majority
voting. In table 7, we present the results of this correspondence analysis.
The number of discordant cases are higher than the number of concordant cases.
We further observe that there are 211 cases where both human and automatic
prediction framework correctly identify the labels; 246 cases where the
automatic prediction framework is only able to identify the correct labels and
humans fail to do so; finally there are non-significant number of cases (only
80) where humans could correctly identify the labels while the automatic
prediction framework failed to do so.

Table 7: Correspondence analysis Concordance |  257
---|---
Discordance |  343
Correctly judged by both human and automatic framework |  211
Wrongly judged by both human and automatic framework |  46
Correctly judged by only human |  80
Correctly judged by only automatic framework |  246

In table 8, we attempt to present reasons behind the above observations. We
find that the human evaluators can correctly label those cases where the
hashtag compound have the highest frequency for the popular class and lowest
for the unpopular class (i.e., the relatively easier cases); on the other
hand, the automatic prediction framework can identify the popular hashtag
compounds whose frequency values are not very different from the constituent
hashtags (i.e., a relatively harder case).

Table 8:  Cause analysis for the correspondence. The cell values represent the
average frequency of the corresponding hashtag types. $HE$ : Human evaluator,
$AF$ : Automatic framework

| Popular | Unpopular
---|---|---
|  #AB |  #A |  #B |  #AB |  #A |  #B
Correctly judged by both $HE$ and $AF$ |  1324.25 |  130.63 |  117.62 |  2.4 |  1369.34 |  1297.47
Wrongly judged by both $HE$ and $AF$ |  1610.33 |  433.25 |  136.58 |  5.77 |  180.7 |  250.23
Correctly judged by only $HE$ |  1644.4 |  460.24 |  332.5 |  1.48 |  576.05 |  949.33
Correctly judged by only $AF$ |  259.3 |  46.3 |  73.3 |  12.24 |  1130.91 |  849.34

##  10 Discussions

In this section, we shall discuss some of the implications of the findings
from our study. The novelty of our research is tied to the method of merging
socio-linguistic features with information technological research. We used
hashtags as linguistic unit. Similar to lexical compounding, hashtag
compounding also exhibit prominence of noun-noun compounding. Unlike lexical
compounds, there are several other forms of compounding prevalent in popular
hashtag compounds like determiner-noun compound, verb-determiner compound. We
perform named entity recognition to identify which kind of named entities
merge. We observe that combinations where both hashtags are entities, are
predominantly found to be person-person combination and product-product
combination whereas other kind of combinations are also prevalent. To the best
of our knowledge, entity combination have not been studied in lexical
compounding and hence is a novel contribution. Apart from the linguistic
aspects of compounding, there are sociological factors which mostly drive the
adoption process of compounds in communities. Due to unavailability of large
scale data, there have been no prior work on this. In our work, we attempt to
study the sociological aspects of hashtag compounding on large-scale Twitter
dataset. We observe that mention and retweeting behavior of individuals are
important factors for popular hashtag compounding. These features appear to be
highly discriminative in predicting popular hashtag compounds. We also compare
our automatic prediction framework with human judgment in order to justify the
hardness of the prediction task. The framework can guide Twitter users in
selecting the right compounds leading to a higher gain in popularity. We also
perform a correspondence analysis of human judgment and machine prediction to
find out whether there is any inherent pattern embedded in it. We find that
human evaluators can guess the relatively easier cases where there are larger
frequency gaps in a compound and its constituent parts whereas the automated
framework can distinguish the more tough cases.

Unlike lexical compounds where there is no/little knowledge of how the
compounding took place and the compounds became popular, there are
sociological influences in the formation of hashtag compounds. Hashtag
compounds can be made popular artificially. For example, #AmazonPrimeDay,
#WWW2015, #KDD2015 etc. There are also spontaneous pressures of hashtag
compound formation. These kind of compounds are generally conversational
hashtags or Idioms. Idioms actually have different spreading mechanism as
shown in [53]. They have high stickiness and low persistence. In table 9, we
further show the differences of these two kinds of hashtag compounds in terms
of some of their statistical properties. The first four compounds are examples
of Idioms ( i.e., spontaneous formation of hashtags) whereas the remaining
four are examples of forced/influenced hashtag compounds. We observe that in
general, the spontaneous compounds have lesser number of mentions per tweets
and lesser no. of collocations with other hashtags compared to the
forced/influenced compounds. Also, the forced hashtag compounds spread via
multiple mentions in early stage of propagation unlike the spontaneous ones.

Table 9: Differences in spontaneous (first four rows) and forced/influenced
(last four rows) hashtag compounds

Hashtag compounds |  no.of mentions per tweet |  no. of retweets per tweet |  no. of collocations per tweet |  no. of mentions in first 50 tweets
---|---|---|---|---
#TheBestFeelingInARelationship |  0.074 |  0.370 |  0.148 |  2
#10WorstFeelings |  0.041 |  0.434 |  0.097 |  0
#YouKnowItsRealWhen |  0.071 |  0.372 |  0.208 |  3
#RelationshipTips |  0.023 |  0.498 |  0.175 |  1
#CMTAwards |  0.446 |  0.225 |  0.401 |  12
#JessicaForTheWin |  0.324 |  0.294 |  0.853 |  11
#SmartGalaxyS3 |  0.495 |  0.430 |  0.183 |  16
#BringBackToonami |  0.565 |  0.214 |  0.159 |  20

##  11 Conclusions and future works

In this paper, we investigated various socio-linguistic properties responsible
for hashtag compound formation and proposed a model to early predict popular
hashtag compounds. To the best of our knowledge, this is the first study which
deals with hashtag compounding and its adoption at a large scale over a very
popular social media.

Our proposed prediction framework achieves a high accuracy of 77.07% with a
high precision and recall. We observe that the tweet content features are most
discriminative compared to others. Among the tweet content features, the
overlap features like n-gram overlap and word overlap are the most significant
ones. The baseline accuracy based on human judgment experiment is only 48.7%.
This indicates that humans are not able to predict popular compound formation
efficiently; in contrast, our model suitably informed with the right set of
discriminative features is able to predict the popular compounds highly
accurately with an overall $\sim$58% improvement on the baseline. We also
perform long term predictions after $T$ = 6 and 10 months after compounding
and achieve 77.5% and 79.13% accuracy respectively. Correspondence analysis of
the results obtained from the human judgments and the automatic framework
shows that while the former is able to distinguish between the relatively
easier cases, the latter is more successful in classifying the harder cases.

There are quite a few other interesting directions that can be explored in
future. One such direction could be to study the lexical compounding on large
scale data available in the form of millions of digitized books and newspaper
archives. This study, we believe, can have important contributions to many NLP
applications.

##  12 Acknowledgments

The authors thank the anonymous reviewers whose suggestions greatly helped
improving the paper. The authors also thank Prof. Chris Biemann, TU Darmstadt
for providing them with a historical Twitter 1% random sample data. This work
has been supported by Microsoft Corporation and Microsoft Research India under
the Microsoft India PhD fellowship award.

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